WO2022156481A1 - Ephemeris forecasting method and apparatus - Google Patents

Abstract

An ephemeris forecasting method and apparatus, relating to the technical field of satellite navigation, and used for reducing ephemeris parameter broadcasting data volume. The method comprises: a server (33) obtaining EOP data and historical ephemeris data of a preset time period (S501); performing satellite orbit forecasting and satellite clock difference forecasting according to the EOP data and the historical ephemeris data to obtain a forecast orbit and a forecast clock difference of a future preset time period (S502); after fitting the forecast orbit into orbit parameters, encoding the orbit parameters of satellites belonging to the same orbit plane to obtain an orbit parameter code of each orbit plane (S503); after fitting the forecast clock difference into a clock difference parameter, encoding the clock difference parameter to obtain a clock difference parameter code (S504); and sending ephemeris parameter codes to a terminal device (31), the ephemeris parameter codes comprising the clock difference parameter code and the orbit parameter code of each orbit plane (S505).

Description

Ephemeris forecast method and device

This application claims the priority of the Chinese patent application with the application number 202110097826.4 and the application name "Ephemeris Forecast Method and Device", which was submitted to the State Intellectual Property Office on January 25, 2021, the entire contents of which are incorporated into this application by reference .

technical field

The present application relates to the technical field of satellite navigation, and in particular, to a method and device for ephemeris forecasting.

Background technique

Currently, terminal positioning needs to use the Global Navigation Satellite System (GNSS) for positioning. Specifically, the terminal device obtains the forecasted orbit and the forecasted clock difference from the server through a network request, and then performs positioning according to the forecasted orbit and the forecasted clock difference.

In the process of broadcasting ephemeris parameters, the server adopts broadcast ephemeris parameters, that is, the server transmits the forecast orbit and forecast clock difference to the terminal equipment in the form of broadcast ephemeris parameters. The broadcast ephemeris parameter has a large amount of data, so that the parameter broadcast data amount is large.

SUMMARY OF THE INVENTION

The embodiments of the present application provide an ephemeris forecasting method and device, which can effectively reduce the amount of ephemeris parameter broadcast data.

In a first aspect, an embodiment of the present application provides an ephemeris forecasting method, which is applied to a server, and the method includes: acquiring EOP data and historical ephemeris data of a preset period; performing satellite orbit forecasting and Predict the satellite clock error to obtain the predicted orbit and predicted clock error of the future preset time period; after fitting the predicted orbit to the orbit parameters, encode the orbit parameters of the satellites belonging to the same orbit plane to obtain the orbit of each orbit plane. Parameter encoding; after fitting the predicted clock error into the clock error parameter, encode the clock error parameter to obtain the clock error parameter encoding; send the ephemeris parameter encoding to the terminal equipment, and the ephemeris parameter encoding includes the clock error parameter encoding and each orbit. The track parameter encoding of the facet.

Based on the above technical solution, after fitting the predicted orbit segments into orbit parameters, the server encodes the orbit parameters based on the orbit plane to obtain the orbit parameter encoding of each orbit plane, and finally broadcasts it in the form of ephemeris parameter encoding. Effectively reduces the amount of parameter broadcast data.

In some possible implementations of the first aspect, the above-mentioned process of performing satellite orbit prediction and satellite clock error prediction according to EOP data and historical ephemeris data, and obtaining the predicted orbit and predicted clock error of a preset time period in the future may include:

Carry out satellite orbit forecast according to satellite orbit data and EOP data, and obtain the forecast orbit for a preset time period in the future;

The clock error forecast is performed according to the satellite clock error data, and the forecast clock error of the future preset time period is obtained; wherein the historical ephemeris data includes satellite orbit data and satellite clock error data.

In some possible implementations of the first aspect, the above-mentioned process of performing satellite orbit prediction according to satellite orbit data and EOP data, and obtaining a predicted orbit in a preset time period in the future may include:

According to the EOP data, the satellite position information in the ground-fixed system in the satellite orbit data is converted into the satellite position information in the inertial system;

Determine the target solar pressure model used by each satellite in the satellite orbit data according to the correspondence between the satellite type and the solar light pressure model;

Based on the target solar light pressure model of each satellite and the satellite position information in the inertial system, the satellite motion equation and variational equation of each satellite are established;

Based on the satellite motion equation and variational equation of each satellite, the reference orbit position and state transition matrix of each satellite at each moment are obtained by using numerical integration;

Based on the satellite orbit data, the reference orbit position and the state transition matrix, the satellite orbit state parameters at the reference time of each satellite are obtained by means of the least squares global solution;

According to the satellite orbit state parameters and the satellite dynamics model at the reference time, the satellite orbit of the future preset time period is obtained by means of numerical integration;

According to the EOP data, the satellite orbit of the future preset time period is converted from the inertial system to the ground-fixed system, and the predicted orbit of the future preset time period is obtained.

In this satellite orbit prediction process, different solar light pressure models are used for different types of satellites, which improves the accuracy of satellite orbit prediction.

In some possible implementations of the first aspect, the correspondence between the satellite type and the solar light pressure model may include:

The solar light pressure model corresponding to GPS satellite or GLNOSS satellite is: ECOM5 parameter model;

The solar light pressure model corresponding to Galileo satellite is: box-wing initial light pressure model and ECOM5 parameter model;

The solar light pressure models corresponding to Beidou GEO satellites are: initial light pressure model, ECOM5 parameter model and periodic empirical acceleration parameters;

The solar light pressure models corresponding to the QZSS satellite are: the initial light pressure model and the ECOM5 parameter model.

In some possible implementations of the first aspect, the above-mentioned satellite orbit data may include precise orbit products for two consecutive days. Here, the optimal fitting time is set to two days to more accurately estimate the satellite orbit dynamics parameters, which further improves the satellite orbit prediction accuracy.

In some possible implementations of the first aspect, the above-mentioned process of performing clock error prediction according to satellite clock error data, and obtaining the forecast clock error of a preset time period in the future may include:

Based on the broadcast ephemeris clock error data, correct the reference deviation of the precision ephemeris clock error data, and obtain the corrected precision ephemeris clock error data. The satellite clock error data includes the broadcast ephemeris clock error data and the precision ephemeris clock error data;

According to the single-day clock error data in the corrected precise ephemeris clock error data of each satellite, the single-day clock speed of each satellite is obtained by fitting;

Based on the single-day clock speed of each satellite, the clock speed time series of each satellite is obtained;

According to the clock speed time series of each satellite, the clock speed change rate of each satellite is obtained by fitting;

According to the initial value of the clock error of each satellite, the clock speed and the rate of change of the clock speed, the predicted clock error of each satellite in the future preset time period is obtained.

In the satellite clock error forecasting process, the broadcast ephemeris clock error data is used to correct the reference deviation of the precision ephemeris clock error data, and then the corrected precision ephemeris clock error data is used for clock error prediction, which can further improve the satellite clock error forecast. reliability and accuracy.

In some possible implementations of the first aspect, the above-mentioned process of correcting the reference deviation of the precise ephemeris clock error data based on the broadcast ephemeris clock error data, and obtaining the corrected precise ephemeris clock error data may include:

Differentiate the broadcast ephemeris clock difference sequence and the precise ephemeris clock difference sequence of the same epoch to obtain the difference sequence of each epoch, wherein the broadcast ephemeris clock difference data includes the broadcast ephemeris clock difference of each epoch Sequence, the precision ephemeris clock error data includes the precise ephemeris clock error sequence of each epoch;

Determine the mean and standard deviation of each difference series;

For each difference sequence, remove the difference points that do not meet the preset conditions in the difference sequence according to the average value and standard deviation, and obtain the target difference sequence;

Calculate the reference deviation according to the target difference sequence;

Comparing the precision ephemeris clock error data with the reference deviation, the corrected precision ephemeris clock error data is obtained.

In this implementation manner, the precise ephemeris clock error data is corrected based on the broadcast ephemeris data, so as to improve the precision of the precise ephemeris clock error data, thereby improving the accuracy and reliability of the subsequent clock error prediction.

In some possible implementations of the first aspect, the above-mentioned process of removing the difference points that do not meet the preset conditions in the difference sequence according to the average value and the standard deviation, and obtaining the target difference sequence may include:

Based on the mean and standard deviation, determine whether each difference point in the difference sequence satisfies

Among them, x is the difference point in the difference series, μ is the mean value of the difference series, and δ is the standard deviation of the difference series;

If it is satisfied, it is determined that the difference points do not meet the preset conditions, and the difference points that do not meet the preset conditions are removed to obtain the difference sequence after eliminating the difference points;

After determining the mean and standard deviation of the difference series after excluding the difference points, take the difference series after excluding the difference points as the difference series, and return to judge each of the difference series based on the mean and standard deviation. Whether the difference point is satisfied

until no difference point in the difference sequence meets the preset condition, then the difference sequence with no difference point meeting the preset condition is used as the target difference sequence.

In some possible implementations of the first aspect, the above-mentioned process of obtaining the rate of change of the clock speed of each satellite by fitting according to the time series of the clock speed of each satellite may include:

For each satellite, use a sliding window to fit the clock speed time series;

During the sliding window sliding process, whenever the number of clock speeds in the sliding window is greater than or equal to the preset number, the clock speed in the sliding window at the current moment and the fitting result obtained by the previous fitting are used to predict the next moment. forecast clock speed;

When the difference between the predicted clock speed at the next moment and the clock speed to be added to the sliding window is less than or equal to the first preset threshold, after adding the clock speed to be added to the sliding window to the sliding window, according to the clock speed in the sliding window The rate of change of the current clock speed is obtained by the speed fitting, and the fitting residual is obtained;

If the fitting residual is less than or equal to the second preset threshold, the current clock rate change rate is used as the clock rate change rate;

If the fitting residual is greater than the second preset threshold, reset the sliding window and continue to slide forward until the clock speed time series is traversed or the fitting residual is less than or equal to the second preset threshold;

When the difference between the clock speed at the next moment and the clock speed to be added to the sliding window is greater than the first preset threshold, reset the sliding window and continue to slide forward until the clock speed time series or the fitting residual is traversed less than or equal to the second preset threshold.

In this implementation, in the process of fitting the clock speed time series by using the sliding window, the size between the fitting residual and the second preset threshold is compared, and the clock speed and the fitting to the window to be added are compared. The size between the predicted clock speeds at the next moment is used to detect and remove abnormal situations in the fitting process, which further improves the accuracy of the clock speed rate of change, thereby improving the accuracy and reliability of clock error prediction.

In a second aspect, an embodiment of the present application provides an ephemeris forecasting system, where the system includes a terminal device and a server.

Wherein, the server may be used to implement the method described in any one of the above-mentioned first aspect.

The terminal device can be used to receive the ephemeris parameter code from the server; decode the ephemeris parameter code to obtain orbit parameters and clock error parameters; determine the position, speed and clock error of the visible GNSS satellites according to the orbit parameters and clock error parameters; Based on the position, velocity and clock offset of the visible GNSS satellites, the current position and/or current velocity is determined.

In a third aspect, an embodiment of the present application provides an ephemeris prediction method, which is applied to a server. The method includes: acquiring EOP data and historical ephemeris data of a preset period; performing satellite orbit prediction and Predict the satellite clock error to obtain the forecast orbit and the forecast clock error for a preset time period in the future; fit the forecast clock error to the clock error parameter; use a polynomial model to fit the forecast orbit to a polynomial coefficient; send the clock error parameter to the terminal device and polynomial coefficients.

The server side of the embodiment of the present application uses a polynomial model to fit the predicted orbit into polynomial coefficients, and broadcasts parameters in the form of polynomial coefficients, which reduces the calculation amount of the terminal equipment in the process of technical GNSS satellite position and velocity.

In some possible implementations of the third aspect, a polynomial model is used above to fit the predicted orbit to polynomial coefficients, including:

Sampling the forecast orbits of each satellite at equal intervals to obtain the sampled forecast orbits of each satellite;

Segment the sampled forecast orbit for each satellite;

According to the basis function and the order of the basis function, each segment of the predicted orbit of each satellite is fitted, and the basis function coefficients are determined, and the basis function coefficients are used as polynomial coefficients, and the polynomial coefficient model includes the basis functions.

In some possible implementations of the third aspect, the basis function of the above polynomial model is:

T ₀ (x)=1, T ₁ (x)=x, T _n (x)=2×T n _-1 (x)-T _n-2 (x), and n is greater than or equal to 2.

Among them, n represents the basis function order.

In some possible implementations of the third aspect, the above-mentioned process of performing satellite orbit prediction and satellite clock error prediction according to EOP data and historical ephemeris data, and obtaining the predicted orbit and predicted clock error of a preset time period in the future may include:

Carry out satellite orbit forecast according to satellite orbit data and EOP data, and obtain the forecast orbit for a preset time period in the future;

According to the satellite clock difference data, the clock difference forecast is carried out, and the forecast clock difference of the preset time period in the future is obtained;

The historical ephemeris data includes satellite orbit data and satellite clock error data.

In some possible implementations of the third aspect, the above-mentioned process of performing satellite orbit prediction according to satellite orbit data and EOP data, and obtaining the predicted orbit in the future preset time period may include:

According to the EOP data, the satellite position information in the ground-fixed system in the satellite orbit data is converted into the satellite position information in the inertial system;

Determine the target solar pressure model used by each satellite in the satellite orbit data according to the correspondence between the satellite type and the solar light pressure model;

Based on the target solar light pressure model of each satellite and the satellite position information in the inertial system, the satellite motion equation and variational equation of each satellite are established;

Based on the satellite motion equation and variational equation of each satellite, the reference orbit position and state transition matrix of each satellite at each moment are obtained by using numerical integration;

Based on the satellite orbit data, the reference orbit position and the state transition matrix, the satellite orbit state parameters at the reference time of each satellite are obtained by means of the least squares global solution;

According to the satellite orbit state parameters and the satellite dynamics model at the reference time, the satellite orbit of the future preset time period is obtained by means of numerical integration;

According to the EOP data, the satellite orbit of the future preset time period is converted from the inertial system to the ground-fixed system, and the predicted orbit of the future preset time period is obtained.

In this satellite orbit prediction process, different solar light pressure models are used for different types of satellites, which improves the accuracy of satellite orbit prediction.

In some possible implementations of the third aspect, the correspondence between the satellite type and the solar light pressure model may include:

The solar light pressure model corresponding to GPS satellite or GLNOSS satellite is: ECOM5 parameter model;

The solar light pressure model corresponding to Galileo satellite is: box-wing initial light pressure model and ECOM5 parameter model;

The solar light pressure models corresponding to Beidou GEO satellites are: initial light pressure model, ECOM5 parameter model and periodic empirical acceleration parameters;

The solar light pressure models corresponding to the QZSS satellite are: the initial light pressure model and the ECOM5 parameter model.

In some possible implementations of the third aspect, the above-mentioned satellite orbit data includes precision orbit products for two consecutive days. Here, the optimal fitting time is set to two days to more accurately estimate the satellite orbit dynamics parameters, which further improves the satellite orbit prediction accuracy.

In some possible implementations of the third aspect, the above-mentioned process of performing clock error prediction according to satellite clock error data, and obtaining the forecast clock error of a preset time period in the future may include:

Based on the broadcast ephemeris clock error data, correct the reference deviation of the precision ephemeris clock error data, and obtain the corrected precision ephemeris clock error data. The satellite clock error data includes the broadcast ephemeris clock error data and the precision ephemeris clock error data;

According to the single-day clock error data in the corrected precise ephemeris clock error data of each satellite, the single-day clock speed of each satellite is obtained by fitting;

Based on the single-day clock speed of each satellite, the clock speed time series of each satellite is obtained;

According to the clock speed time series of each satellite, the clock speed change rate of each satellite is obtained by fitting;

According to the initial value of the clock error of each satellite, the clock speed and the rate of change of the clock speed, the predicted clock error of each satellite in the preset time period is obtained.

In the satellite clock error forecasting process, the broadcast ephemeris clock error data is used to correct the reference deviation of the precision ephemeris clock error data, and then the corrected precision ephemeris clock error data is used for clock error prediction, which can further improve the satellite clock error forecast. reliability and accuracy.

In some possible implementations of the third aspect, the above-mentioned process of correcting the reference deviation of the precise ephemeris clock error data based on the broadcast ephemeris clock error data, and obtaining the corrected precise ephemeris clock error data may include:

Differentiate the broadcast ephemeris clock difference sequence and the precise ephemeris clock difference sequence of the same epoch to obtain the difference sequence of each epoch, wherein the broadcast ephemeris clock difference data includes the broadcast ephemeris clock difference of each epoch Sequence, the precision ephemeris clock error data includes the precise ephemeris clock error sequence of each epoch;

Determine the mean and standard deviation of each difference series;

For each difference sequence, remove the difference points that do not meet the preset conditions in the difference sequence according to the average value and standard deviation, and obtain the target difference sequence;

Calculate the reference deviation according to the target difference sequence;

Comparing the precision ephemeris clock error data with the reference deviation, the corrected precision ephemeris clock error data is obtained.

In this implementation manner, the precise ephemeris clock error data is corrected based on the broadcast ephemeris data, so as to improve the precision of the precise ephemeris clock error data, thereby improving the accuracy and reliability of the subsequent clock error prediction.

In some possible implementations of the third aspect, the above-mentioned process of removing the difference points that do not meet the preset conditions in the difference sequence according to the average value and the standard deviation, and obtaining the target difference sequence may include:

Based on the mean and standard deviation, determine whether each difference point in the difference sequence satisfies

Among them, x is the difference point in the difference series, μ is the mean value of the difference series, and δ is the standard deviation of the difference series;

If it is satisfied, it is determined that the difference points do not meet the preset conditions, and the difference points that do not meet the preset conditions are removed to obtain the difference sequence after eliminating the difference points;

After determining the mean and standard deviation of the difference series after excluding the difference points, take the difference series after excluding the difference points as the difference series, and return to judge each of the difference series based on the mean and standard deviation. Whether the difference point is satisfied

until no difference point in the difference sequence meets the preset condition, then the difference sequence with no difference point meeting the preset condition is used as the target difference sequence.

In some possible implementations of the third aspect, the above-mentioned process of obtaining the rate of change of the clock speed of each satellite by fitting according to the time series of the clock speed of each satellite may include:

For each satellite, use a sliding window to fit the clock speed time series;

During the sliding window sliding process, whenever the number of clock speeds in the sliding window is greater than or equal to the preset number, the clock speed in the sliding window at the current moment and the fitting result obtained by the previous fitting are used to predict the next moment. forecast clock speed;

When the difference between the predicted clock speed at the next moment and the clock speed to be added to the sliding window is less than or equal to the first preset threshold, after adding the clock speed to be added to the sliding window to the sliding window, according to the clock speed in the sliding window The rate of change of the current clock speed is obtained by the speed fitting, and the fitting residual is obtained;

If the fitting residual is less than or equal to the second preset threshold, the current clock rate change rate is used as the clock rate change rate;

If the fitting residual is greater than the second preset threshold, reset the sliding window and continue to slide forward until the clock speed time series is traversed or the fitting residual is less than or equal to the second preset threshold;

When the difference between the clock speed at the next moment and the clock speed to be added to the sliding window is greater than the first preset threshold, reset the sliding window and continue to slide forward until the clock speed time series or the fitting residual is traversed less than or equal to the second preset threshold.

In this implementation, in the process of fitting the clock speed time series by using the sliding window, the size between the fitting residual and the second preset threshold is compared, and the clock speed and the fitting to the window to be added are compared. The size between the predicted clock speeds at the next moment is used to detect and remove abnormal situations in the fitting process, which further improves the accuracy of the clock speed rate of change, thereby improving the accuracy and reliability of clock error prediction.

In a fourth aspect, an embodiment of the present application provides an ephemeris prediction method, which is applied to a terminal device. The method includes: receiving polynomial coefficients and clock error parameters from a server; determining the position and velocity of visible GNSS satellites according to the polynomial coefficients; The clock error parameter determines the clock error of the visible GNSS satellites; determines the current position and/or speed according to the positions, speeds and clock errors of the visible GNSS satellites.

The embodiment of the present application uses a polynomial model to fit the predicted orbit into polynomial coefficients, which effectively reduces the amount of computation in the positioning process of the terminal device, thereby reducing the power consumption in the GNSS positioning process.

In some possible implementations of the fourth aspect, the above process of determining the position and velocity of the visible GNSS satellites according to the polynomial coefficients may include:

Determine the position of the visible GNSS satellites based on the polynomial coefficients and the basis functions of the polynomial model;

The velocity of the visible GNSS satellites is determined from the polynomial coefficients and the basis function derivatives of the polynomial model.

Exemplarily, the basis function of the above polynomial model is:

T ₀ (x)=1, T ₁ (x)=x, T _n (x)=2×T n _-1 (x)-T _n-2 (x), and n is greater than or equal to 2.

Among them, n represents the order of the basis function;

Based on the basis functions, the positions of the GNSS satellites are:

Among them, x(t), y(t) and z(t) represent the three-dimensional position of the satellite.

The basis function derivative is:

F ₀ (x)=0, F ₁ (x)=1, F _n (x)=2T _n-1 (x)+2xF _n-1 (x)-F _n-2 (x), n greater than or equal to 2.

Based on the basis function derivatives, the velocity of the GNSS satellite is:

In a fifth aspect, the embodiments of the present application provide an ephemeris forecasting system, and the system may include a server and a terminal device.

Wherein, the server may be used to implement the method described in any one of the third aspect above. The terminal device may be used to implement the method described in any one of the fourth aspect above.

In a sixth aspect, an embodiment of the present application provides a server, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the first aspect or the third aspect when executing the computer program any of the methods.

In a seventh aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, any one of the above-mentioned fourth aspects is implemented. Methods.

In an eighth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any one of the first aspect, the third aspect, or the fourth aspect is implemented item method.

In a ninth aspect, an embodiment of the present application provides a chip system, the chip system includes a processor, the processor is coupled to a memory, and the processor executes a computer program stored in the memory, so as to implement the first aspect, the third aspect, or the third aspect above. The method of any one of the four aspects. The chip system may be a single chip, or a chip module composed of multiple chips.

In a tenth aspect, an embodiment of the present application provides a computer program product that, when the computer program product runs on an electronic device, causes the electronic device to execute the method described in any one of the first, third, or fourth aspects.

It can be understood that, for the beneficial effects of the foregoing second aspect to the tenth aspect, reference may be made to the relevant description in the foregoing first aspect, which will not be repeated here.

Description of drawings

1 is a schematic diagram of the architecture of a standard AGNSS system provided by an embodiment of the present application;

2 is a schematic block diagram of the architecture of the PGNSS system provided by the embodiment of the present application;

3 is a schematic block diagram of the system architecture of the ephemeris forecasting solution provided by the embodiment of the present application;

FIG. 4 is a schematic structural block diagram of a terminal device 31 provided by an embodiment of the present application;

5 is a schematic flowchart of a method for ephemeris forecasting provided by an embodiment of the present application;

6 is a schematic block diagram of a satellite orbit prediction process provided by an embodiment of the present application;

7 is a schematic block diagram of a satellite clock error forecasting process provided by an embodiment of the present application;

8 is a schematic block diagram of a reference deviation correction process for precise ephemeris clock error data provided by an embodiment of the present application;

9 is a 235-day clock speed time series of some satellites provided by the embodiment of the present application;

10 is a schematic diagram of an orbital ephemeris parameter encoding method based on an orbital plane;

FIG. 11 is another schematic flowchart of the ephemeris forecasting method provided by the embodiment of the present application;

12 is a schematic block diagram of the structure of the ephemeris forecasting device provided by the embodiment of the present application;

FIG. 13 is another schematic structural block diagram of the ephemeris forecasting device provided by the embodiment of the application;

FIG. 14 is another schematic block diagram of the structure of the ephemeris forecasting apparatus provided by the embodiment of the present application.

Detailed ways

With the wide application of terminal devices such as mobile phones, smart wearable devices, tablet computers, and car machines, location-based services (LBS) have also gained more and more attention. When using LBS, the terminal device needs to be positioned first to determine the current location.

The terminal equipment is generally positioned based on the Global Navigation Satellite System (GNSS). Specifically, when the terminal device initiates a positioning request, it uses the built-in GNSS chip to demodulate a complete set of ephemeris from the satellite navigation signal, and then completes positioning based on the complete ephemeris.

At present, when a terminal device performs positioning based on GNSS, if the Time To First Fix (TTFF) is too long, the user experience may be affected. In order to reduce TTFF and improve user experience, an Assisted GNSS (AGNSS, AGNSS) technology is proposed. AGNSS technology can be divided into standard AGNSS service and Extended Ephemeris service. The standard AGNSS and the predicted GNSS (Predicted GNSS, PGNSS) will be exemplarily introduced below.

(1) Standard AGNSS. Referring to FIG. 1 , it is a schematic diagram of the architecture of the standard AGNSS system provided by the embodiment of the present application. As shown in FIG. 1 , the system may include a terminal device 11 , a data exchange center 12 , an AGNSS server 13 , a GNSS observation station 14 and a GNSS satellite 15 .

The GNSS observation station 14 is used for acquiring the GNSS signals of the GNSS satellites 15 , and demodulating the broadcast ephemeris parameters from the GNSS signals in real time; and then sending the demodulated broadcast ephemeris parameters to the AGNSS server 13 .

The AGNSS server 13 is configured to receive the broadcast ephemeris parameters sent by the GNSS observation station 14 and store the broadcast ephemeris parameters. In addition, the AGNSS server 13 is further configured to send broadcast ephemeris parameters to the terminal device 11 in response to the positioning request after receiving the positioning request from the terminal device 11 .

The terminal device 11 is used to initiate a positioning request, and send the positioning request to the AGNSS service 13 through the data exchange center 12 . In addition, the terminal device 11 is further configured to receive the broadcast ephemeris parameters returned by the AGNSS server 13, and perform positioning based on the broadcast ephemeris parameters.

Exemplarily, the terminal positioning process based on the standard GNSS system includes:

In each positioning process, the terminal device 11 sends a positioning request to the AGNSS server 13 through the network, and the positioning request is used to obtain broadcast ephemeris parameters. After receiving the positioning request from the terminal device 11 , the AGNSS server 13 sends the broadcast ephemeris parameters corresponding to the positioning request to the terminal device 11 through the network.

The terminal device 11 receives the broadcast ephemeris parameters returned by the AGNSS server 13 through the network, and based on the broadcast ephemeris parameters, uses the built-in GNSS chip to calculate the positions and velocities of all visible GNSS satellites; then based on the positions and velocities of the GNSS satellites, and GNSS observations to determine current position and/or current speed. That is, the terminal device can determine the current location based on GNSS, or determine the current speed, or determine the current location and the current speed.

(2) PGNSS. Referring to FIG. 2 , it is a schematic block diagram of the architecture of the PGNSS system provided by the embodiment of the present application. As shown in FIG. 2 , the system may include a terminal device 21 , a data exchange center 22 , a PGNSS server 23 , a data source server 24 and a GNSS satellite 25 .

Among them, the data exchange center 22 is used to transmit data. The data source server 24 is used to store the data source, and the data source may be, but not limited to, precise ephemeris data, broadcast ephemeris data or raw carrier phase observations. The PGNSS server 23 is configured to acquire the data source stored on the data source server 24 and generate forecast ephemeris data based on the data source.

The terminal device 21 is configured to acquire the forecast ephemeris data generated by the PGNSS server 23, and determine its current location and/or current speed based on the forecast ephemeris data.

Exemplarily, the terminal positioning process based on the PGNSS system includes:

After acquiring the data source, the PGNSS server 23 performs modeling based on the data source to obtain a satellite orbit prediction model and a satellite clock error prediction model; then, it performs satellite orbit prediction based on the satellite orbit prediction model, obtains the predicted orbit, and predicts the satellite clock error based on the satellite orbit prediction model. The model performs satellite clock error forecasting to obtain the forecast clock error; and then fits the forecast orbit and the forecast clock error into broadcast ephemeris parameters and transmits it to the terminal device 21 .

After the terminal device 21 obtains the broadcast ephemeris parameters, it can periodically inject the broadcast ephemeris parameters into the GNSS chip, so as to use the GNSS chip and the broadcast ephemeris parameters to calculate the position, speed and clock error of the visible GNSS satellites, and based on the visible GNSS satellites Position, velocity and clock offset, as well as pseudorange and carrier phase observations, etc., determine current position and/or current velocity.

In the above-mentioned standard AGNSS and PGNSS, the server uses broadcast ephemeris parameters in the process of broadcasting ephemeris parameters, that is, the server transmits the forecast orbit and forecast clock difference to the terminal in the form of broadcast ephemeris parameters. equipment. The broadcast ephemeris parameter has a large amount of data, so that the parameter broadcast data amount is large.

In order to reduce the amount of parameter broadcast data in ephemeris prediction, an embodiment of the present application proposes an orbital plane-based ephemeris parameter broadcast scheme. In the orbit-plane-based ephemeris parameter broadcasting scheme, the server obtains the ephemeris parameter encoding of the same orbital plane by encoding the ephemeris parameters of the satellites belonging to the same orbital plane, and in the form of ephemeris parameter encoding, the forecast The orbit and forecast clock difference are transmitted to the terminal equipment. Compared with the traditional broadcast in the form of broadcast ephemeris parameters, the amount of parameter broadcast data is greatly reduced.

The technical solutions provided by the embodiments of the present application will be described in detail below. In the following description, for the purpose of illustration rather than limitation, specific details such as a specific system structure and technology are set forth in order to provide a thorough understanding of the embodiments of the present application.

Referring to FIG. 3 , it is a schematic block diagram of the system architecture of the ephemeris prediction solution provided by the embodiment of the present application. As shown in FIG. 3 , the system may include a terminal device 31 and a server 33 , and the terminal device 31 and the server 33 are connected through a network 32 .

The terminal device 31 is a device with a wireless transceiver function, which may be a handheld terminal device, a vehicle, a vehicle-mounted terminal, a smart wearable device, or other computing devices. Exemplarily, the terminal device is a portable terminal device such as a mobile phone or a tablet computer; it can also be an augmented reality (AR) device, a virtual reality (VR) device, or an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), etc. This embodiment of the present application does not limit the specific type of the terminal device 31 . FIG. 3 exemplarily shows that the terminal device 31 is a mobile phone.

Different types of terminal devices 31 may have different specific structures. Referring to the schematic block diagram of the structure of the terminal device 31 provided by the embodiment of the present application shown in FIG. 4 , the terminal device 31 may include, but is limited to, a processor 311 , a GNSS chip 312 , and a memory 313 , and both the GNSS chip 312 and the memory 313 communicate with the processor 311 connect. Of course, the terminal device 31 also includes a GNSS antenna for transmitting and receiving GNSS signals.

Processor 311 may include one or more processing units. For example, the processor 311 may include an application processor (application processor, AP), a modem processor, a controller, a baseband processor, and the like. Wherein, different processing units may be independent devices, or may be integrated in one or more processors.

The controller may be the nerve center and command center of the terminal device 31 . The controller can generate an operation control signal according to the instruction operation code and timing signal, and complete the control of fetching and executing instructions.

Memory 313 may be used to store computer executable program code, which includes instructions. The processor 311 executes various functional applications and data processing of the terminal device 31 by executing the instructions stored in the memory 313 . The memory 313 may include a storage program area and a storage data area. The storage program area may store an operating system, an application program required for at least one function, and the like. The storage data area can store data and the like created during the use of the terminal device 31 . For example, after receiving the broadcast ephemeris parameters from the server, the terminal device 31 stores the broadcast ephemeris parameters in the storage data area, periodically reads the broadcast ephemeris parameters from the storage data area, and injects them into the GNSS chip 312 .

In addition, the memory 313 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, universal flash storage (UFS), and the like.

Through the GNSS antenna and the GNSS chip 312, the terminal device 31 can realize the reception and transmission of GNSS signals, as well as GNSS positioning and/or speed determination.

In this embodiment of the present application, the GNSS may include a global positioning system (GPS), a global navigation satellite system (GLONASS), a Beidou navigation satellite system (BDS), a quasi-zenith Satellite systems (quasi-zenith satellite system, QZSS) and/or satellite-based augmentation systems (satellite based augmentation systems, SBAS).

It can be understood that the structures illustrated in the embodiments of the present application do not constitute a specific limitation on the terminal device 31 . In other embodiments of the present application, the terminal device 31 may include more or less components than shown, or combine some components, or separate some components, or arrange different components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

Exemplarily, when the terminal device 31 is a smart wearable device, the smart wearable device may be a smart bracelet, a smart watch, or smart glasses, or the like. At this time, the terminal device 31 may also include a sensor, a display screen, and the like, and the sensor may include a photoelectric sensor and a physiological sensor.

Exemplarily, when the terminal device 31 is a mobile phone, the terminal device 31 may further include a charging management module, a power management module, a battery, a mobile communication module, an audio module, a speaker, a receiver, a microphone, an earphone interface, a sensor module, a button, and a motor. , indicator, camera, display screen, and user identification module (subscriber identification module, SIM) card interface, etc.

The server 32 may include one or more servers, and in general, the server 32 mainly includes a PGNSS server.

In a specific application, the server 32 is used to obtain a data source, perform satellite orbit prediction and satellite clock error prediction based on the data source, and then convert the predicted orbit and predicted clock error into certain parameters, and transmit the parameters to the terminal device 31 .

The terminal device 31 is used to obtain the predicted orbit and the predicted clock difference from the server 32 through a network request, and use the GNSS chip to calculate the position and speed of the visible GNSS satellites according to the predicted orbit and the predicted clock difference, and then based on the position and speed of the GNSS satellites. etc., to determine its current location and/or current speed.

The embodiments of the present application may be applicable to scenarios of navigation and positioning and/or speed measurement. For example, the terminal device 31 is a vehicle-mounted terminal, and the vehicle-mounted terminal uses GNSS to perform vehicle positioning and vehicle speed measurement. For another example, the terminal device 31 is a mobile phone, and the application of the mobile phone uses LBS to recommend products to the user. The embodiments of the present application do not limit application scenarios.

After introducing the system structures and application scenarios that may be involved in the embodiments of the present application, the technical solutions of the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

Referring to FIG. 5 , it is a schematic flowchart of the ephemeris prediction method provided by the embodiment of the present application. As shown in Figure 5, the process can include the following steps:

Step S501, the server acquires EOP data and historical ephemeris data of a preset period.

It should be noted that the above-mentioned historical ephemeris data may include precise ephemeris data, may include real-time broadcast ephemeris data, or may include original carrier phase observations. The historical ephemeris data may refer to a data source in PGNSS.

Typically, the above historical ephemeris data includes satellite orbit data and satellite clock error data. The satellite orbit data includes the satellite position information of each satellite at each time, and the satellite clock error data includes the satellite clock error information of each satellite at each time. Satellite orbit data and Earth Orientation Parameters (Earth Orientation Parameters, EOP) data can be used for satellite orbit prediction, and the EOP data is obtained by the server from outside. Satellite clock error data can be used for satellite clock error prediction.

For example, when the historical ephemeris data includes precise ephemeris data, the precise ephemeris data may include precise orbital products and precise clock error products. Precision orbit products and EOP files are used for satellite orbit prediction, and precision clock error products are used for satellite clock error prediction.

In specific applications, the server can download precision orbit products and precision clock error products from the File Transfer Protocol (FTP) server of the International GNSS Service (IGS), and download precision orbit products and precision clock error products from the International Earth Rotation Service (International Earth Rotation Service, IERS) server to download the EOP file.

The preset time period can be set according to actual needs. For example, the preset time period is 2 days, that is, the server obtains historical ephemeris data for 2 days.

When acquiring satellite orbit data, the server may acquire satellite orbit data of one or more days, and perform satellite orbit prediction according to the satellite orbit data of one or more days. Optionally, in this embodiment of the present application, the optimal fitting duration may be set to two days, so as to estimate the dynamic parameters of the satellite orbit more accurately and improve the accuracy of satellite orbit prediction. That is to say, the server obtains the satellite orbit data for two consecutive days, and uses the satellite orbit data for two consecutive days for satellite orbit prediction. Higher precision.

Step S502, the server performs satellite orbit prediction and satellite clock error prediction according to the EOP data and the historical ephemeris data, and obtains the predicted orbit and the predicted clock error of a preset time period in the future.

It can be understood that the server may use the satellite orbit data in the EOP data and the historical ephemeris data to perform satellite orbit prediction, and use the satellite clock error data in the historical ephemeris data to perform satellite clock error prediction.

It should be noted that the future preset time period can be set according to actual needs. For example, the future preset time period can be 7 to 28 days, that is, the server can predict satellite orbits and satellite clock errors in the next 7 to 28 days.

The following describes the satellite orbit prediction process and the satellite clock error prediction process respectively.

In some embodiments, in order to improve the accuracy of satellite orbit prediction, the embodiments of the present application propose an improved satellite orbit prediction process. The satellite orbit prediction process can use different solar light pressure models for different types of satellites. The satellite orbit forecasting process can refer to the schematic block diagram of the satellite orbit forecasting process shown in FIG. 6 . As shown in FIG. 6 , the process can include the following steps:

Step S601, the server converts the satellite position information in the ground-fixed system to the satellite position information in the inertial system based on the EOP data.

Wherein, the satellite orbit data includes satellite position information under the ground-fixed system. The satellite orbit data may be a precision orbit product.

It should be noted that, in order to estimate the dynamic parameters of the satellite orbit more accurately, the server can perform satellite orbit prediction based on the satellite orbit data of two consecutive days, that is, the optimal fitting estimation time period is set to two days.

Step S602, the server determines the target solar pressure model corresponding to each satellite in the satellite orbit data according to the correspondence between the satellite type and the solar pressure model.

It should be noted that the correspondence between the satellite type and the solar light pressure model is preset, that is, the solar light pressure model used by each type of satellite is predetermined.

Exemplarily, the correspondence between the satellite type and the solar light pressure model may be shown in Table 1 below.

Table 1

According to the above Table 1, when the satellite type of a certain satellite is GPS satellite or GLONASS satellite, the target solar light pressure model of this satellite is: ECOM5 parameter model. Similarly, when the satellite type of a satellite is a QZSS satellite, the target light pressure model of the satellite is: the initial light pressure model and the ECOM5 parameter model.

It can be understood that the satellite orbit data includes the three-dimensional position information of each satellite at each moment, and the satellite orbit data includes identification information of each satellite, for example, the identification information may be a satellite number. The server determines the satellite type of each satellite in the satellite orbit data according to the identification information such as the satellite number in the satellite orbit data; and then based on the satellite type of each satellite, according to the preset correspondence between the satellite type and the solar light pressure model, Determine the target solar light pressure model corresponding to each satellite.

In comparison, using different solar light pressure models for different types of satellites can improve the accuracy of satellite orbit prediction.

Step S603, the server establishes the satellite motion equation and the variational equation of each satellite according to the target solar light pressure model of each satellite and the satellite position information in the inertial system.

It can be understood that, for a single satellite, the server establishes the satellite motion equation and the variational equation of the satellite according to the target solar light pressure model of the satellite and the satellite position information in the inertial system of the satellite.

Step S604: Based on the motion equation and variational equation of each satellite, the server obtains the reference orbital position, velocity and state transition matrix of each satellite at each moment by means of numerical integration.

Among them, in the process of obtaining the reference orbit position, velocity and state transition matrix at each moment through numerical integration, the server needs to obtain the reference orbit position, velocity and state transition matrix at the current moment based on information such as the reference orbit position, velocity and state transition matrix at the previous moment. Velocity and state transition matrices.

Step S605, the server obtains the satellite orbit state parameters of each satellite at the reference time by means of the least squares overall solution based on the satellite orbit data, the reference orbit position and the state transition matrix. The satellite orbit state parameters at the reference time may include, but are not limited to, the satellite position, velocity, dynamic model parameters and empirical force parameters at the reference time.

Step S606, the server obtains the satellite orbit of the future preset time period by means of numerical integration according to the satellite orbit state parameter and the satellite dynamics model at the reference time. At this time, the obtained satellite orbit in the future preset time period is the satellite orbit in the inertial system.

Step S607 , the server converts the satellite orbits in the future preset time period under the inertial system to the ground-fixed system according to the EOP data, and obtains the predicted orbits in the future preset time period.

In a specific application, the server may convert the satellite orbit of the inertial system obtained in step S606 to the ground-fixed system according to the EOP prediction value in the EOP data.

It can be seen from the above that in this satellite orbit prediction process, different solar light pressure models are used for different types of satellites, which improves the accuracy of satellite orbit prediction. Further, the optimal fitting duration is set to two days to more accurately estimate the satellite orbit dynamics parameters and further improve the satellite orbit prediction accuracy.

Of course, in other embodiments, the satellite orbit prediction can also be performed by using the existing satellite orbit prediction method.

That is to say, for the satellite orbit prediction process, the server may use the improved satellite orbit prediction process proposed in the embodiment of the present application, or may use the existing satellite orbit prediction process.

Similarly, for the satellite clock error prediction process, the server may use the improved satellite clock error prediction process proposed in the embodiment of the present application, or may use the existing satellite clock error prediction process. The improved satellite clock error prediction process proposed in the embodiment of the present application is shown in FIG. 7 .

Referring to the schematic block diagram of the satellite clock error prediction process provided by the embodiment of the present application shown in FIG. 7 , the process may include the following steps:

Step S701, the server corrects the reference deviation of the precision ephemeris clock error data based on the broadcast ephemeris clock error data, and obtains the corrected precision ephemeris clock error data, and the satellite clock error data includes the broadcast ephemeris clock error data and the precision ephemeris clock error data. poor data.

It should be noted that the clock error of the atomic clock carried by the GNSS satellite is usually described by a first-order polynomial or a second-order polynomial. Exemplarily, the broadcast ephemeris clock difference may be shown in the following formula (1).

Among them, clk ⁿ (t) is the satellite clock error at time t.

is the clock offset at the initial time t ₀ ,

is the clock speed at the initial time t ₀ ,

is the clock drift at the initial time t ₀ . ε(t) is the uncertainty component of random variation.

The precise ephemeris clock difference data can be shown in the following formula (2).

Among them, b(t) is the reference deviation.

In comparison, the precision ephemeris clock error data has higher accuracy than the broadcast ephemeris clock error data, and a higher precision forecast clock error can be generated based on the precise ephemeris clock error data. However, compared with the broadcast ephemeris clock error, there is a time-varying reference deviation b(t). In addition, the reference deviation b(t) of the precise ephemeris clock error data from different sources is different.

The reference deviation b(t) will affect the accuracy of the clock error prediction. In order to further improve the accuracy of the satellite clock error prediction, the precise ephemeris clock error data can be corrected before using the precise ephemeris clock error data for satellite clock error prediction. Then use the corrected precise ephemeris clock error data to forecast the satellite clock error.

In some embodiments, referring to the schematic block diagram of the reference deviation correction process of the precise ephemeris clock difference data shown in FIG. 8 , the correction process may include the following steps:

Step S801, the server makes a difference between the broadcast ephemeris clock difference sequence and the precise ephemeris clock difference sequence of the same epoch, and obtains the difference value sequence of each epoch, wherein the broadcast ephemeris clock difference data includes the broadcast of each epoch. Ephemeris clock offset sequence, the precise ephemeris clock offset data includes the precise ephemeris clock offset sequence of each epoch.

Exemplarily, the GPS broadcast ephemeris clock error and the precise clock error in a certain epoch correspond to 32 clock error sequences (that is, 32 stars) respectively, wherein the broadcast ephemeris clock error sequence is:

The precise ephemeris clock sequence is:

At this time, the difference between the broadcast ephemeris clock difference sequence and the precise ephemeris clock difference sequence under this epoch is obtained, and the difference value sequence under this epoch is obtained as:

It can be understood that each epoch corresponds to a sequence of difference values.

Step S802, the server determines the average and standard deviation of each difference sequence.

Specifically, after the difference value sequence of each epoch is obtained, for each difference value sequence, the average value and standard deviation of the difference value sequence are calculated.

Exemplarily, the sequence of differences in an epoch is:

The average is:

The standard deviation is:

Step S803: For each difference sequence, the server removes the difference points that do not meet the preset conditions in the difference sequence according to the average value and the standard deviation, and obtains the target difference sequence.

In some embodiments, an iterative manner may be used to eliminate abnormal differences in the difference sequence through 3sigma. Specifically, for each difference sequence, based on the average value and the standard deviation, determine whether each difference point in the difference sequence satisfies the

Among them, x is the difference point in the difference series, μ is the mean value of the difference series, and δ is the standard deviation of the difference series.

It is understandable that there are multiple difference points in the difference sequence. Based on the standard deviation and average value of the difference sequence, it is judged whether each difference point in the difference sequence satisfies the

If a certain difference point is satisfied

Then, it is determined that the difference point does not meet the preset conditions, the difference point is considered to be an abnormal difference point, and the difference point is removed. According to this, each difference point is judged, and the difference value sequence after eliminating the difference point is obtained.

After the first round of the abnormal difference elimination process is completed, the next round of abnormal difference elimination process is performed based on the difference sequence after the elimination of the difference points.

In the next round of abnormal difference elimination process, the server calculates the average value and standard deviation of the difference value sequence after the difference value point is eliminated, and according to the average value and the standard deviation, judges the difference value sequence after eliminating the difference value point. Whether the difference points are satisfied

If it is satisfied, the corresponding difference points are eliminated. According to this, each difference point is judged, and the difference sequence after eliminating the difference point is obtained again.

The above abnormal difference elimination process is iteratively performed until no difference point is eliminated in a certain difference sequence, and the difference sequence is the target difference sequence.

In the iterative process, after the first round of abnormal difference removal process, it can be regarded as the difference value sequence after removing the difference value points as the difference value sequence, and return to judge the difference value sequence based on the average value and standard deviation. Whether each difference point of satisfies

until no difference point in the difference sequence meets the preset condition, then the difference sequence with no difference point meeting the preset condition is used as the target difference sequence.

For example, a sequence of differences is

There are a total of 32 difference points in the difference sequence.

First, calculate the average and standard deviation of the difference series, and judge whether the 32 difference points satisfy

At this point, it is assumed that there are 2 difference points that satisfy

Then, these two difference points are considered to be abnormal difference points, and these two abnormal difference points are eliminated to obtain the difference sequence after eliminating the difference points. Here, the difference value sequence after removing the difference value points includes 30 difference value points. So far, the first round of abnormal difference point elimination process is completed.

Then, the next round of abnormal difference point elimination process is performed. At this time, calculate the average value and standard deviation of the difference value series including 30 difference value points, and based on the average value and standard deviation, respectively judge whether the 30 difference value points satisfy

At this point, it is assumed that there is one difference point that satisfies

Then the difference point is considered to be an abnormal difference point, and the abnormal difference point is removed to obtain the difference sequence after removing the difference point. The difference sequence after removing the difference point here includes 29 difference values. point.

Then, the next round of abnormal difference point elimination process is performed. At this time, calculate the average value and standard deviation of the difference value series including 29 difference value points, and based on the average value and standard deviation, respectively judge whether the 29 difference value points satisfy

At this point, it is assumed that there is one difference point that satisfies

Then, the difference point is considered to be an abnormal difference point, and the abnormal difference point is eliminated to obtain the difference sequence after eliminating the difference point. Here, the difference sequence after eliminating the difference point includes 28 difference values. point.

Then, the next round of abnormal difference point elimination process is performed. At this time, calculate the average value and standard deviation of the difference value series including 28 difference value points, and based on the average value and standard deviation, respectively determine whether the 28 difference value points satisfy

At this point, it is assumed that no difference point is satisfied

That is, for the difference points that are not eliminated, the difference sequence including 28 difference points is used as the target difference sequence.

It should be noted that, in practical applications, only one round of abnormal difference point elimination process may be performed.

Step S804, the server calculates the reference deviation according to the target difference sequence.

Illustratively, the reference deviation is calculated by the following formula (3).

Among them, N represents the number of clock differences included in the target difference sequence. For example, after multiple rounds of abnormal difference point elimination, there are still 28 difference points in the target sequence, that is, the target sequence includes 28 difference values. point, at this time, N=28.

Step S805, the server makes a difference between the precise ephemeris clock error data and the reference deviation, and obtains the corrected precise ephemeris clock error data.

Specifically, the server may subtract the reference deviation from the precise ephemeris clock error data in the satellite clock error data to obtain the corrected precise ephemeris clock error data.

It should be noted that correcting the precise ephemeris clock error data based on the broadcast ephemeris clock error data can further improve the accuracy and reliability of satellite clock error prediction.

Step S702, the server obtains the single-day clock speed of each satellite by fitting according to the single-day clock error data in the corrected precise ephemeris clock error data of each satellite.

It is understandable that the corrected precision ephemeris clock error data includes the clock error data of each satellite for one or more days. Based on the single-day clock error data of each satellite, a single-day fitting is performed for each satellite. Get the daily clock speed.

In some embodiments, the server may first perform gross error detection on the single-day clock error data in the corrected precise ephemeris clock error data, to obtain the precision ephemeris clock error data after gross error detection.

During gross error detection, if the a ₀ item or a ₁ item in the original clock error time series jumps, the original clock error time series is segmented, and the gross error is eliminated at the same time to obtain the gross error detection. Satellite clock error data. The original clock offset time series here refers to the corrected precise ephemeris clock offset data, the a ₀ item refers to the clock offset, and the a ₁ item refers to the clock speed.

In this embodiment of the present application, the gross error detection method may be arbitrary, for example, the gross error detection method may be median detection.

It should be noted that, in some other embodiments, the server may not perform gross error detection on the clock error data. However, in comparison, gross error detection can eliminate gross errors in clock error data, making the subsequent clock error prediction more accurate.

After gross error detection, the server uses the precise ephemeris clock error data after gross error detection as an observation value, and establishes a first-order polynomial model or a quadratic polynomial model for each satellite. Exemplarily, the observation equation of a certain satellite is shown in the following formula (4).

Among them, a ₀ refers to the clock difference, and a ₁ refers to the clock speed.

Based on the single-day clock error data of each satellite, the server uses the least squares method to fit the clock errors for a single day, and calculates the daily clock error model coefficients of each satellite. For example, taking the above formula (4) as an example, a single-day fitting is performed on the clock error, and the daily clock error model coefficients a ₀ and a ₁ of each satellite are obtained.

It is understandable that, if no gross error detection is performed, the corrected precise ephemeris clock error data is directly used as the observation value to fit the single-day clock speed of each satellite.

Step S703, the server obtains the clock speed time series of each satellite based on the single-day clock speed of each satellite.

It can be understood that, based on the satellite clock error data of each satellite for each day, the server can obtain the clock error model coefficients a ₀ and a ₁ of each satellite for multiple days. Exemplarily, FIG. 9 shows a 235-day clock speed time series for some satellites.

Step S704, the server obtains the rate of change of the clock speed of each satellite by fitting according to the time series of the clock speed of each satellite.

In some embodiments, for each satellite, a sliding window can be used to fit the clock speed time series, that is, a sliding window is used to slide in the clock speed time series. When the number of clock speeds is greater than or equal to the preset number, the forecast clock speed at the next moment is predicted according to the clock speed in the sliding window at the current moment and the fitting result obtained from the previous fitting.

Then, determine whether the difference between the forecast clock speed at the next moment and the clock speed to be added to the sliding window is less than or equal to the first preset threshold, if the difference between the forecast clock speed and the clock speed to be added to the sliding window is Less than or equal to the first preset threshold, the clock speed to be added to the sliding window can participate in the next fitting process of the rate of change of the clock speed, that is, after adding the clock speed to be added to the sliding window, according to the The clock speed is fitted to obtain the clock speed change rate and fitting residual of the current fitting.

It should be noted that the clock speed to be added to the sliding window is the real clock speed at the next moment. For example, the clock speed time series of a certain satellite includes 5 clock speeds, and these five clock speeds are b ₁ , b ₂ , b ₃ , b ₄ , b ₅ in chronological order, that is, on the clock speed time series, The clock speed value corresponding to t1 is b ₁ , the clock speed value corresponding to t ₂ is b ₂ , the clock speed value corresponding to t ₃ is b ₃ , the clock speed value corresponding to t ₄ is _{b 4} , and the clock speed value corresponding to t ₅ The value is b ₅ . _At time t ₄ , the clock speeds contained in the sliding window are b ₁ , b ₂ , b ₃ , and b ₄ . The predicted clock speed is B ₅ . At this time, the clock speed to be added to the sliding window is b ₅ , and the difference between the predicted clock speed B ₅ and the actual clock speed b ₅ at time t ₅ is calculated to determine whether the difference is less than or equal to the first preset threshold.

Assuming that the difference between the predicted clock speed B ₅ and the real clock speed b ₅ at time t ₅ is less than or equal to the first preset threshold, then b ₅ is added to the sliding window. At this time, the clock speed included in the sliding window For b ₁ , b ₂ , b ₃ , b ₄ and b ₅ , perform fitting according to b ₁ , b ₂ , b ₃ , b ₄ and b ₅ in the sliding window at this moment, and obtain the current clock speed of the current fitting Rate of change and fit residuals.

After obtaining the rate of change of the current clock speed and the fitting residual, it is further judged whether the fitting residual is less than or equal to the second preset threshold, and if the fitting residual is less than or equal to the second preset threshold, the current clock speed The rate of change is the rate of change of the satellite's clock speed. Conversely, if the fitting residual is greater than the second preset threshold, it is considered that a gross error or a ₁ item jumps, and after resetting the sliding window, it continues to slide forward, and during the sliding process, the fitting is performed according to the above process. The combination and judgment are performed until the clock speed time series is traversed or the fitting residual is less than or equal to the second preset threshold.

If the difference between the forecast clock speed and the clock speed to be added to the sliding window is greater than the first preset threshold, reset the sliding window and continue to slide forward until the clock speed time series is traversed or the fitting residual is less than or equal to the second preset threshold.

It should be noted that, the above-mentioned first preset threshold value, second preset threshold value, and preset number can be set according to actual needs, which are not limited herein.

For example, the size of the sliding window is 5, that is, the number of clock speeds that can be accommodated in the sliding window is preset to 5. In addition, set when the number of clock speeds in the sliding window is greater than or equal to 3, start fitting and forecasting according to the clock speeds in the sliding window, that is, the preset number is 3, whenever the number of clock speeds in the sliding window is greater than or equal to 3 When it is equal to or equal to 3, it starts to fit and predict according to the clock speed in the sliding window.

Assume that the clock speed time series of a certain satellite includes 100 clock speeds, that is, the clock speed values corresponding to each time t ₁ , t ₂ , t ₃ , ..., t ₁₀₀ are b ₁ , b ₂ , b ₃ , ..., b respectively ₁₀₀ . At the initial moment, the number of clock speeds in the sliding window is 0, that is, there is no clock speed in the sliding window; the sliding window continues to slide forward, and it is judged whether the number of clock speeds contained in the sliding window is greater than or equal to 3; at a certain moment , the sliding window includes b ₅ , b ₆ , b ₇ , b ₈ and b ₉ , a total of 5 clock speed values. At this time, according to the five clock speeds of b ₅ , b ₆ , b ₇ , b ₈ and b ₉ value, the forecast clock speed B ₁₀ at the next time (ie, t ₁₀ ) is obtained.

Then, the difference between the predicted clock speed B ₁₀ and the actual clock speed b ₁₀ is calculated, and it is judged whether the difference is less than or equal to the first preset threshold, and if the difference is less than or equal to the first preset threshold, the The real clock speed b ₁₁ at the next moment is added to the sliding window. At this time, since the maximum number of clock speeds in the sliding window is 5, one clock speed value needs to be removed, and then the clock speed b ₁₀ at the next moment is added to the sliding window. Assuming that after adding b ₁₀ to the sliding window, the clock speed values contained in the sliding window are b ₆ , b ₇ , b ₈ , b ₉ and b ₁₀ , then fit according to the clock speed values in the sliding window at this moment to obtain the clock speed Rate of change and fit residuals. Then, it is further judged whether the fitting residual of the current fitting is less than or equal to the second preset threshold.

If the difference between the predicted clock speed B ₁₀ and the actual clock speed b ₁₀ is greater than the first preset threshold, the sliding window is reset. Resetting the sliding window means clearing the number of clock speeds in the sliding window, that is, Clock speed is not included in the reset sliding window. After resetting the sliding window, the sliding window continues to slide forward according to the sliding step size, and during the sliding process, it continues to judge whether the number of clock speeds in the sliding window is greater than or equal to 3. If the number of clock speeds in the sliding window at a certain moment is is greater than or equal to 3, according to the clock speed in the sliding window at this moment, fit and forecast the forecast clock speed at the next moment, and predict the difference between the clock speed and the real clock speed, and judge the difference between the first preset The size of the threshold. This cycle is repeated until the clock speed time series is traversed, or the fitting residual of a certain clock speed change rate fitting process is less than or equal to the second preset threshold.

It can be seen from the above that in this embodiment, the clock speed change rate is fitted based on the clock speed time series, and the abnormal detection of the fitted forecasted clock speed and the fitting residual is performed through a sliding window, which improves the accuracy of the clock speed change rate, and further improves the accuracy of the clock speed change rate. The accuracy and reliability of satellite clock error prediction are improved.

Of course, in some other embodiments, the existing clock rate change rate fitting method may also be used.

Step S705 , the server obtains the predicted clock error of each satellite in the future preset time period according to the initial value of the clock error, the clock speed and the rate of change of the clock speed of each satellite.

In a specific application, after the server fits the clock speed change rate based on the clock speed time series of each satellite, the satellite clock error a ₀ (ie, the initial value of the clock error) at the reference time, the fitted clock speed and the clock speed The rate of change is used as a parameter to forecast the satellite clock error, and the forecast clock error of each satellite in the future preset time period is obtained.

It can be seen from the above that the improved satellite clock error prediction process proposed in the embodiments of the present application can further improve the accuracy and reliability of satellite clock error prediction.

Step S503: After the server fits the predicted orbit to the orbit parameters, it encodes the orbit parameters of the satellites belonging to the same orbit plane, and obtains the orbit parameter code of each orbit plane.

Step S504: After the server fits the predicted clock difference into the clock difference parameter, the server encodes the clock difference parameter to obtain the clock difference parameter code.

In a specific application, for the predicted clock difference, after fitting the predicted clock difference into a clock difference parameter, the server encodes the clock difference parameter to obtain the clock difference parameter code.

For the forecasted orbit, in order to send the forecasted orbit to the terminal device, the server can use a set of Kepler orbital parameters to perform segmental fitting on the satellite forecasted orbit to obtain the orbital parameters of each segment of the forecasted orbit.

The process for the server to fit the predicted orbit to the orbit parameters can be as follows:

First, the server samples the forecast orbit at equal intervals to obtain the satellite position of each sampling point. The sampling interval can be set according to actual needs. For example, the sampling interval can be 5 minutes, that is, the server samples every 5 minutes. At this time, assuming that the initial sampling point is T ₀ , the predicted orbit position corresponding to time T ₀ is ( x ₀ , y ₀ , z ₀ ), the next sampling point is T ₁ , T ₁ =T ₀ +5, the predicted orbit position corresponding to time T ₁ is (x ₁ , y ₁ , z ₁ ), and so on, until Sampling is complete. After the sampling is completed, a plurality of sampling points and the predicted orbit position corresponding to each sampling point are obtained.

Then, the server segments multiple sampling points according to time, and obtains segmentation results. Exemplarily, 4 hours are used for segmentation, and the segmentation results obtained by sampling at 5-minute intervals are shown in Table 2.

Table 2

As shown in Table 2 above, there are a total of 240 minutes in 4 hours, and sampling is performed every 5 minutes, so there are 49 sampling points in total, which are T ₀ , T ₁ , ..., T ₄₈ , and the predicted orbit positions corresponding to time T ₀ is (x ₀ , y ₀ , z ₀ ), and the predicted orbital position corresponding to time T ₄₈ is (x ₄₈ , y ₄₈ , z ₄₈ ).

After sampling and segmenting the predicted orbits, a 16-parameter broadcast ephemeris model or an 18-parameter broadcast ephemeris model can be used to fit each segment of the predicted orbit to obtain the broadcast ephemeris parameters corresponding to each segment of the predicted orbit. Fitting algorithms such as the least squares method can be used in the fitting process. Exemplarily, the 16-parameter broadcast ephemeris model may be as shown in Table 3 below.

table 3

It should be noted that, after fitting the predicted orbit segments into orbit parameters, the server can directly send the orbit parameters to the terminal device. However, the orbit ephemeris parameter has a large amount of data, and directly sending the orbit ephemeris parameter to the terminal device will result in a large amount of parameter broadcast data.

In order to reduce the amount of parameter broadcast data without increasing the complexity of coding, the embodiment of the present application proposes an orbital plane-based ephemeris parameter broadcast method, which encodes the orbital ephemeris parameters that belong to the same orbital plane, and obtains each orbital plane. Orbital ephemeris parameter encoding for each orbital plane. Among them, the orbital ephemeris parameters of satellites on the same orbital plane have strong consistency, so the orbital ephemeris parameters of the same orbital plane can be encoded.

Specifically, the server first determines the satellites belonging to the same orbital plane, and then encodes the orbital parameters corresponding to the satellites belonging to the same orbital plane to obtain the encoding of the orbital ephemeris parameters of the orbital plane. Exemplarily, referring to the schematic diagram of the orbital plane-based orbital ephemeris parameter encoding method shown in FIG. 10 , as shown in FIG. 10 , after encoding the original broadcast ephemeris parameters of orbital plane 1, the ephemeris parameters of orbital plane 1 are obtained. Encoding 1, in the same way, after encoding the original broadcast ephemeris parameters of the orbital plane N, the ephemeris parameter encoding N of the orbital plane N is obtained. The original broadcast ephemeris parameter refers to the ephemeris parameter obtained by segmental fitting of the predicted orbit by the server using the Kepler orbit parameter.

After obtaining the ephemeris parameter code 1, ..., the ephemeris parameter code N, the server sends the ephemeris parameter code 1, ..., the ephemeris parameter code N to the terminal device instead of sending the original broadcast ephemeris parameters to the terminal device. In this way, the amount of ephemeris parameter broadcast data can be reduced. Exemplarily, Table 4 below shows the comparison of the amount of data broadcast based on the original broadcast ephemeris parameters and broadcast based on the orbit plane.

Table 4

It can be seen from Table 4 above that the broadcast method based on the orbit plane has a smaller amount of data than the broadcast method based on the original broadcast ephemeris parameters.

It should be noted that when encoding the original ephemeris parameters of the same orbital plane, the original ephemeris parameters of one of the satellites can be encoded normally, and then based on the normally encoded ephemeris parameters of the satellite, the same The ephemeris parameters of other satellites on the orbital plane are incrementally encoded, and finally the ephemeris parameter encoding of the orbital plane is obtained.

For example, orbital plane 1 includes three satellites, select one of the three satellites as the target satellite, and perform normal encoding on the original broadcast ephemeris parameters of the target satellite to obtain the orbital ephemeris parameter encoding of the target satellite. For two satellites other than the target satellite, incremental encoding is performed based on the ephemeris parameter encoding of the target satellite.

Step S505, the server sends the ephemeris parameter code to the terminal device, and the ephemeris parameter code includes the clock error parameter code and the orbit parameter code of each orbit plane.

It can be understood that the terminal device can obtain the predicted orbit and the predicted clock difference from the server through a network request. After the server receives the request from the terminal device, in response to the request, the server may send the clock error parameter code and the orbit parameter code of each track plane to the terminal device. After the terminal device obtains the ephemeris parameter code, it decodes the orbit parameter code to obtain the orbit ephemeris parameters of each satellite, and then calculates the position, velocity and clock of all visible GNSS satellites based on the orbit ephemeris parameters and clock error parameters. difference, and finally determine the current position and/or the current speed according to the position and speed of the visible GNSS satellites, as well as the observation amount, etc.

It can be seen from the above that in the embodiment of the present application, after fitting the predicted orbit segments into orbit ephemeris parameters, the orbit ephemeris parameters are encoded based on the orbit plane to obtain the orbit ephemeris parameter encoding of each orbit plane, which can effectively Reduce the amount of parameter advertisement data.

Further, the embodiment of the present application also provides an improved satellite orbit prediction process, which improves the accuracy of satellite orbit prediction.

Further, the embodiment of the present application also provides an improved satellite clock error prediction process, which improves the accuracy and reliability of satellite clock error prediction.

In some embodiments, the embodiments of the present application provide another ephemeris prediction scheme. Referring to FIG. 11, another schematic flowchart of the ephemeris prediction method provided by the embodiment of the present application, the ephemeris prediction method may include the following steps:

Step S1101, the server acquires EOP data and historical ephemeris data of a preset period.

Step S1102, the server performs satellite orbit prediction and satellite clock error prediction according to the EOP data and the historical ephemeris data, and obtains the predicted orbit and the predicted clock error for a preset time period in the future.

It should be noted that, for the related introduction of step S1101 and step S1102, reference may be made to step S501 and step S502 above, which will not be repeated here.

Wherein, during the satellite orbit forecasting, the server may use the existing satellite orbit forecasting method to perform satellite orbit forecasting, and may also use the improved satellite orbit forecasting method provided by the embodiments of the present application to perform satellite orbit forecasting.

When forecasting the satellite clock error, the server may use the existing satellite clock error forecasting method to perform the satellite clock error forecasting, and may also use the improved satellite clock error forecasting method provided by the embodiment of the present application to perform the clock error forecasting. The improved satellite orbit prediction process can be seen in Figure 6 and related content above, and the improved satellite clock error prediction process can be seen in Figure 7 and related content above, which will not be repeated here.

Step S1103, the server uses a polynomial model to fit the predicted orbit into polynomial coefficients, and fits the predicted clock offset into clock offset parameters.

It should be noted that the polynomial model includes a basis function and coefficients of the basis function. For example, the polynomial model is a Chebyshev polynomial model, and its basis function is:

T ₀ (x)=1, T ₁ (x)=x, T _n (x)=2xT _n-1 (x)-T _n-2 (x), n is greater than or equal to 2(5)

Among them, n represents the basis function order.

GNSS satellite positions can be represented using basis functions. For example, when the basis function is the above formula (5), the satellite position can be:

Among them, x(t), y(t) and z(t) represent the three-dimensional position of the satellite.

The process by which the server uses polynomials to fit the predicted orbits to polynomial coefficients may include:

First, the server samples and segments the forecast orbits at equal intervals to obtain segment results. For the introduction of sampling and segmentation, reference may be made to the relevant content in step S503 above, which will not be repeated here.

Then, after selecting a suitable basis function order n, the server uses the basis function to represent the satellite position of each sampling point, so as to determine the basis function coefficient corresponding to the satellite position of each sampling point.

For example, the segmentation results of a certain segment of the predicted orbit are shown in Table 2. The above formula (6) is used to represent the predicted orbit positions corresponding to time T ₀ , T ₁ , ..., T ₄₈ respectively, and based on the X coordinate corresponding to each time value, Y coordinate value and Z coordinate value, determine the basis function coefficients corresponding to T ₀ , T ₁ , . . . , T ₄₈ .

Additionally, the server fits the predicted clock offset to the clock offset parameters.

Step S1104, the server sends the polynomial coefficients and clock error parameters to the terminal device.

It can be understood that the terminal device can obtain the predicted orbit and the predicted clock difference from the server through a network request. After receiving the request from the terminal device, the server may send the polynomial coefficient and the clock error parameter to the terminal device in response to the request.

Specifically, the terminal device receives the polynomial coefficients and clock error parameters from the generator. After the terminal device obtains the polynomial coefficients, it can calculate the satellite clock errors of the visible GNSS satellites according to the clock error parameters, and calculate the GNSS satellites according to the basis functions and the polynomial coefficients. The position of the GNSS satellite is calculated from the polynomial coefficients and the derivatives of the basis functions.

Among them, the basis function derivative can be exemplified as:

F ₀ (x)=0, F ₁ (x)=1, F _n (x)=2T _n-1 (x)+2xF _n-1 (x)-F _n-2 (x), n greater than or equal to 2(7)

Using the basis function derivative shown in Equation (7), calculating the GNSS satellite velocity can be shown in Equation (8) below:

It should be noted that using the polynomial model to fit the predicted orbit into polynomial coefficients can reduce the amount of computation, time-consuming computation, and power consumption of the GNSS chip during the positioning process of the terminal device.

Specifically, when the server uses Kepler parameters to fit the predicted orbit segments into broadcast ephemeris parameters, after receiving the server's broadcast ephemeris parameters, the terminal device will calculate the ephemeris parameters of all visible GNSS satellites based on the broadcast ephemeris parameters. position and speed. On the one hand, in the process of calculating the position and velocity of a single GNSS satellite based on the broadcast ephemeris parameters, many complex floating-point operations are involved, which in turn leads to a large amount of computation and occupies a lot of processor resources. On the other hand, the number of visible GNSS satellites is as high as 50 or more per current positioning epoch. It can be seen that the greater the number of GNSS satellites, the greater the amount of computation. For example, assuming that the terminal device locates once per second, the terminal device needs to perform more than 50 GNSS satellite position and velocity calculation processes per second, which requires a huge amount of computation and high power consumption.

That is to say, the terminal device calculates the position and speed of the GNSS satellite based on the broadcast ephemeris parameters, which requires a large amount of computation, takes a long time for computation, and consumes a high power of the GNSS chip.

However, in the embodiment of the present application, by using a polynomial model to fit the predicted orbit segments into polynomial coefficients, after receiving the polynomial coefficients, the terminal device only needs to calculate the positions of the GNSS satellites according to the polynomial coefficients and basis functions. The satellite speed can be calculated by the derivative of the basis function, and the calculation process involves less floating-point operations, which reduces the amount of calculation, reduces the time-consuming of the calculation, and further reduces the power consumption of the GNSS chip.

In addition, in the segmental fitting process of the forecast orbit, the fitting error will increase with the increase of the fitting time. In order to ensure the accuracy, in the existing AGNSS and PGNSS schemes, the fitting duration of each group of broadcast ephemeris parameters is usually 4 hours.

However, in the embodiment of the present application, a polynomial model is used to perform segmental fitting of the predicted orbit. When the order n of the basis function is 18, under the same accuracy, the validity period of each set of parameters can be as long as 12 hours, which means that GNSS The chip can use a lower parameter update frequency.

It can be seen from the above that for the terminal device side, the embodiment of the present application uses a polynomial model to fit the predicted orbit into polynomial coefficients, which effectively reduces the amount of computation in the terminal device positioning process, thereby reducing the power consumption in the GNSS positioning process. .

Further, the embodiment of the present application also provides an improved satellite orbit prediction process, which improves the accuracy of satellite orbit prediction.

Further, the embodiment of the present application also provides an improved satellite clock error prediction process, which improves the accuracy and reliability of satellite clock error prediction.

It should be understood that the size of the sequence numbers of the steps in the above embodiments does not mean the sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

In this embodiment of the present application, the terminal and the server can be divided into functional modules according to the above method examples. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one processing module. The above-mentioned integrated modules can be implemented in the form of hardware, and can also be implemented in the form of software function modules. It should be noted that, the division of modules in the embodiments of the present application is schematic, and is only a logical function division, and there may be other division manners in actual implementation. The following is an example of dividing each function module corresponding to each function to illustrate:

12 shows a schematic block diagram of the structure of the ephemeris forecasting apparatus provided by the embodiment of the present application, the ephemeris forecasting apparatus may be applied to a server, and the ephemeris forecasting apparatus may include:

The first acquisition module 121 is configured to acquire EOP data and historical ephemeris data of a preset period.

The first forecasting module 122 is used to perform satellite orbit forecast and satellite clock error forecast according to EOP data and historical ephemeris data, and obtain the forecast orbit and forecast clock difference of a preset time period in the future.

The orbit parameter encoding module 123 is used to encode the orbit parameters of satellites belonging to the same orbit plane after fitting the predicted orbit into orbit parameters to obtain the orbit parameter encoding of each orbit plane;

The clock difference parameter encoding module 124 is configured to encode the clock difference parameter after fitting the predicted clock difference into the clock difference parameter to obtain the clock difference parameter code;

The first sending module 125 is configured to send the ephemeris parameter code to the terminal device, where the ephemeris parameter code includes the clock error parameter code and the orbit parameter code of each orbit plane.

In some possible implementations, the above-mentioned first forecasting module 122 specifically includes:

The first orbit forecasting unit is used to predict the satellite orbit according to the satellite orbit data and the EOP data, and obtain the forecast orbit of the future preset time period;

The first clock error forecasting unit is used to predict the clock error according to the satellite clock error data, and obtain the forecast clock error of a preset time period in the future;

The historical ephemeris data includes satellite orbit data and satellite clock error data.

In some possible implementations, the above-mentioned first orbit prediction unit is specifically used to: convert the satellite position information in the satellite orbit data in the ground-fixed system into the satellite position information in the inertial system according to the EOP data; according to the satellite type and The corresponding relationship of the solar light pressure model is to determine the target solar light pressure model used by each satellite in the satellite orbit data; based on the target solar light pressure model of each satellite and the satellite position information in the inertial system, the satellite of each satellite is established. Motion equation and variational equation; based on the satellite motion equation and variational equation of each satellite, obtain the reference orbital position and state transition matrix of each satellite at each moment by using numerical integration; based on satellite orbital data, reference orbital position and the state transition matrix, the satellite orbit state parameters of each satellite at the reference time are obtained by means of the least squares global solution; according to the satellite orbit state parameters at the reference time and the satellite dynamics model, the future preset time period is obtained by numerical integration According to the EOP data, the satellite orbit of the future preset time period is converted from the inertial system to the ground-fixed system, and the predicted orbit of the future preset time period is obtained.

In some possible implementation manners, the correspondence between the above-mentioned satellite types and the solar light pressure model may include: the solar light pressure model corresponding to the GPS satellite or the GLNOSS satellite is: ECOM5 parameter model; the solar light pressure model corresponding to the Galileo satellite is: box -wing initial light pressure model and ECOM5 parameter model; the solar light pressure model corresponding to Beidou GEO satellite is: initial light pressure model, ECOM5 parameter model and periodic empirical acceleration parameter; the solar light pressure model corresponding to QZSS satellite is: initial light pressure model and the ECOM5 parametric model.

In some possible implementations, the above-mentioned satellite orbit data may include precision orbit products for two consecutive days.

In some possible implementation manners, the above-mentioned first clock error prediction unit is specifically used for: correcting the reference deviation of the precise ephemeris clock error data based on the broadcast ephemeris clock error data, and obtaining the corrected precise ephemeris clock error data, and the satellite The clock error data includes broadcast ephemeris clock error data and precision ephemeris clock error data; according to the single-day clock error data in the corrected precise ephemeris clock error data of each satellite, the single-day clock of each satellite is obtained by fitting. Based on the single-day clock speed of each satellite, the clock speed time series of each satellite is obtained; according to the clock speed time series of each satellite, the clock speed change rate of each satellite is obtained by fitting; The initial value of the difference, the clock speed, and the rate of change of the clock speed are used to obtain the forecast clock error of each satellite in a preset time period in the future.

In some possible implementations, the above-mentioned first clock error prediction unit is specifically used to: make a difference between the broadcast ephemeris clock error sequence and the precise ephemeris clock error sequence of the same epoch to obtain the difference value sequence of each epoch , wherein the broadcast ephemeris clock error data includes the broadcast ephemeris clock error sequence of each epoch, and the precise ephemeris clock error data includes the precise ephemeris clock error sequence of each epoch; determine the average value and standard of each difference sequence Difference; for each difference sequence, remove the difference points that do not meet the preset conditions in the difference sequence according to the average value and standard deviation, and obtain the target difference sequence; calculate the reference deviation according to the target difference sequence; The calendar clock error data and the reference deviation are compared to obtain the corrected precise ephemeris clock error data.

In some possible implementations, the above-mentioned first clock error prediction unit is specifically configured to: based on the average value and the standard deviation, determine whether each difference value point in the difference value sequence satisfies the

Among them, x is the difference point in the difference sequence, μ is the mean value of the difference sequence, and δ is the standard deviation of the difference sequence; if it is satisfied, it is determined that the difference point does not meet the preset conditions, and the unsatisfactory conditions are removed. The difference point after excluding the difference point is obtained, and the difference value sequence after excluding the difference value point is obtained; after determining the average value and standard deviation of the difference value series after excluding the difference value point, the difference value sequence after excluding the difference value point is regarded as the difference value sequence, and Returns, based on the mean and standard deviation, to determine whether each difference point in the difference series satisfies

until no difference point in the difference sequence meets the preset condition, then the difference sequence with no difference point meeting the preset condition is used as the target difference sequence.

In some possible implementations, the above-mentioned first clock error prediction unit is specifically used to: for each satellite, use a sliding window to fit the clock speed time series; in the sliding window sliding process, whenever the clock in the sliding window is When the number of clock speeds is greater than or equal to the preset number, the forecast clock speed at the next moment is predicted according to the clock speed in the sliding window at the current moment and the fitting result obtained from the previous fitting; When the difference between the clock speeds of the sliding window is less than or equal to the first preset threshold, after adding the clock speed to be added to the sliding window to the sliding window, the current clock speed change rate is obtained according to the clock speed fitting in the sliding window , and the fitting residual is obtained; if the fitting residual is less than or equal to the second preset threshold, the current clock rate change rate is taken as the clock rate change rate; if the fitting residual is greater than the second preset threshold, the After setting the sliding window, continue to slide forward until the clock speed time series is traversed or the fitting residual is less than or equal to the second preset threshold; the difference between the clock speed at the next moment and the clock speed to be added to the sliding window is greater than When the first preset threshold is set, reset the sliding window and continue to slide forward until the clock speed time series is traversed or the fitting residual is less than or equal to the second preset threshold.

The above-mentioned ephemeris forecasting device has the function of realizing the above-mentioned ephemeris forecasting method, and this function can be realized by hardware, and can also be realized by executing corresponding software by hardware, and the hardware or software includes one or more modules corresponding to the above-mentioned functions, and the modules can be is software and/or hardware.

It should be noted that the information exchange, execution process and other contents between the above-mentioned devices/modules are based on the same concept as the method embodiments of the present application. For specific functions and technical effects, please refer to the method embodiments section. It is not repeated here.

Referring to FIG. 13, another schematic structural block diagram of the ephemeris prediction device provided by the embodiment of the present application is shown. The ephemeris prediction device may be applied to a server, and the ephemeris prediction device may include:

The second acquiring module 131 is configured to acquire EOP data and historical ephemeris data of a preset period.

The second forecasting module 132 is configured to perform satellite orbit forecasting and satellite clock error forecasting according to EOP data and historical ephemeris data, and obtain forecasted orbits and forecasted clock offsets for a preset time period in the future.

The clock error fitting module 133 is used for fitting the predicted clock error into a clock error parameter.

The polynomial fitting module 134 is used to fit the predicted trajectory to polynomial coefficients using a polynomial model.

The second sending module 135 is configured to send the clock difference parameter and the polynomial coefficient to the terminal device.

In some possible implementations, the above-mentioned polynomial fitting module 134 is specifically configured to: sample the predicted orbits of each satellite at equal intervals to obtain the sampled predicted orbits of each satellite; to sample the predicted orbits of each satellite Segmentation is performed; each segment of the predicted orbit of each satellite is fitted according to the basis function and the order of the basis function, the basis function coefficients are determined, and the basis function coefficients are used as polynomial coefficients, and the polynomial coefficient model includes the basis functions.

In some possible implementations, the basis functions of the above polynomial model are: T ₀ (x)=1, T ₁ (x)=x, T _n (x)=2xT _n-1 (x)-T _n-2 (x), n is greater than or equal to 2, where n represents the order of the basis function.

In some possible implementations, the above-mentioned second forecast module 132 includes:

The second orbit forecasting unit is used to predict the satellite orbit according to the satellite orbit data and the EOP data, and obtain the forecast orbit of the future preset time period;

The second clock error forecasting unit is used to predict the clock error according to the satellite clock error data, and obtain the forecast clock error of a preset time period in the future;

The ephemeris data includes satellite orbit data and satellite clock error data.

In some possible implementations, the above-mentioned second orbit prediction unit is specifically used to: convert the satellite position information in the satellite orbit data under the ground-fixed system into the satellite position information under the inertial frame according to the EOP data; according to the satellite type and The corresponding relationship of the solar light pressure model is to determine the target solar light pressure model used by each satellite in the satellite orbit data; based on the target solar light pressure model of each satellite and the satellite position information in the inertial system, the satellite of each satellite is established. Motion equation and variational equation; based on the satellite motion equation and variational equation of each satellite, obtain the reference orbital position and state transition matrix of each satellite at each moment by using numerical integration; based on satellite orbital data, reference orbital position and the state transition matrix, the satellite orbit state parameters of each satellite at the reference time are obtained by means of the least squares global solution; according to the satellite orbit state parameters at the reference time and the satellite dynamics model, the future preset time period is obtained by numerical integration According to the EOP data, the satellite orbit of the future preset time period is converted from the inertial system to the ground-fixed system, and the predicted orbit of the future preset time period is obtained.

In some possible implementation manners, the correspondence between the above-mentioned satellite types and the solar light pressure model may include: the solar light pressure model corresponding to the GPS satellite or the GLNOSS satellite is: ECOM5 parameter model; the solar light pressure model corresponding to the Galileo satellite is: box -wing initial light pressure model and ECOM5 parameter model; the solar light pressure model corresponding to Beidou GEO satellite is: initial light pressure model, ECOM5 parameter model and periodic empirical acceleration parameter; the solar light pressure model corresponding to QZSS satellite is: initial light pressure model and the ECOM5 parametric model.

In some possible implementations, the above-mentioned satellite orbit data includes precision orbit products for two consecutive days.

In some possible implementations, the above-mentioned second clock error forecasting unit is specifically used for: correcting the reference deviation of the precise ephemeris clock error data based on the broadcast ephemeris clock error data, and obtaining the corrected precise ephemeris clock error data, the satellite The clock error data includes broadcast ephemeris clock error data and precision ephemeris clock error data; according to the single-day clock error data in the corrected precise ephemeris clock error data of each satellite, the single-day clock of each satellite is obtained by fitting. Based on the single-day clock speed of each satellite, the clock speed time series of each satellite is obtained; according to the clock speed time series of each satellite, the clock speed change rate of each satellite is obtained by fitting; The initial value of the difference, the clock speed, and the rate of change of the clock speed are used to obtain the forecast clock error of each satellite in a preset time period in the future.

In some possible implementations, the above-mentioned second clock error prediction unit is specifically used to: make a difference between the broadcast ephemeris clock error sequence and the precise ephemeris clock error sequence of the same epoch to obtain the difference value sequence of each epoch , wherein the broadcast ephemeris clock error data includes the broadcast ephemeris clock error sequence of each epoch, and the precise ephemeris clock error data includes the precise ephemeris clock error sequence of each epoch; determine the average value and standard of each difference sequence Difference; for each difference sequence, remove the difference points that do not meet the preset conditions in the difference sequence according to the average value and standard deviation, and obtain the target difference sequence; calculate the reference deviation according to the target difference sequence; The calendar clock error data and the reference deviation are compared to obtain the corrected precise ephemeris clock error data.

In some possible implementations, the above-mentioned second clock error prediction unit is specifically configured to: based on the average value and the standard deviation, determine whether each difference value point in the difference value sequence satisfies the

Among them, x is the difference point in the difference sequence, μ is the mean value of the difference sequence, and δ is the standard deviation of the difference sequence; if it is satisfied, it is determined that the difference point does not meet the preset conditions, and the unsatisfactory conditions are removed. The difference point after excluding the difference point is obtained, and the difference value sequence after excluding the difference value point is obtained; after determining the average value and standard deviation of the difference value series after excluding the difference value point, the difference value sequence after excluding the difference value point is regarded as the difference value sequence, and Returns, based on the mean and standard deviation, to determine whether each difference point in the difference series satisfies

until no difference point in the difference sequence meets the preset condition, then the difference sequence with no difference point meeting the preset condition is used as the target difference sequence.

In some possible implementations, the above-mentioned second clock error prediction unit is specifically used to: for each satellite, use a sliding window to fit the clock speed time series; in the sliding window sliding process, whenever the clock in the sliding window is When the number of clock speeds is greater than or equal to the preset number, the forecast clock speed at the next moment is predicted according to the clock speed in the sliding window at the current moment and the fitting result obtained from the previous fitting; When the difference between the clock speeds of the sliding window is less than or equal to the first preset threshold, after adding the clock speed to be added to the sliding window to the sliding window, the current clock speed change rate is obtained according to the clock speed fitting in the sliding window , and the fitting residual is obtained; if the fitting residual is less than or equal to the second preset threshold, the current clock rate change rate is taken as the clock rate change rate; if the fitting residual is greater than the second preset threshold, the After setting the sliding window, continue to slide forward until the clock speed time series is traversed or the fitting residual is less than or equal to the second preset threshold; the difference between the clock speed at the next moment and the clock speed to be added to the sliding window is greater than When the first preset threshold is set, reset the sliding window and continue to slide forward until the clock speed time series is traversed or the fitting residual is less than or equal to the second preset threshold.

The above-mentioned ephemeris forecasting device has the function of realizing the above-mentioned ephemeris forecasting method, and this function can be realized by hardware, and can also be realized by executing corresponding software by hardware, and the hardware or software includes one or more modules corresponding to the above-mentioned functions, and the modules can be is software and/or hardware.

It should be noted that the information exchange, execution process and other contents between the above-mentioned devices/modules are based on the same concept as the method embodiments of the present application. For specific functions and technical effects, please refer to the method embodiments section. It is not repeated here.

Referring to FIG. 14, another schematic structural block diagram of the ephemeris prediction apparatus provided by the embodiment of the present application is shown. The ephemeris prediction apparatus may be applied to a terminal device, and the ephemeris prediction apparatus may include:

The receiving module 141 is configured to receive polynomial coefficients and clock error parameters from the server.

The first determination module 142 is configured to determine the position and velocity of the visible GNSS satellites according to the polynomial coefficients.

The second determination module 143 is configured to determine the clock difference of the visible GNSS satellites according to the clock difference parameter;

The third determining module 144 is configured to determine the current position and/or velocity according to the position, velocity and clock offset of the visible GNSS satellites.

In some possible implementations, the above-mentioned first determining module 142 is specifically configured to:

Determine the position of the visible GNSS satellites based on the polynomial coefficients and the basis functions of the polynomial model;

The velocity of the visible GNSS satellites is determined from the polynomial coefficients and the basis function derivatives of the polynomial model.

Among them, the basis function of the above polynomial model is:

T ₀ (x)=1, T ₁ (x)=x, T _n (x)=2×T n _-1 (x)-T _n-2 (x), and n is greater than or equal to 2.

Among them, n represents the order of the basis function;

Based on the basis functions, the positions of the GNSS satellites are:

Among them, x(t), y(t) and z(t) represent the three-dimensional position of the satellite.

The basis function derivative is:

F ₀ (x)=0, F ₁ (x)=1, F _n (x)=2T _n-1 (x)+2xF _n-1 (x)-F _n-2 (x), n greater than or equal to 2.

Based on the basis function derivatives, the velocity of the GNSS satellite is:

The above-mentioned ephemeris forecasting device has the function of realizing the above-mentioned ephemeris forecasting method, and this function can be realized by hardware, and can also be realized by executing corresponding software by hardware, and the hardware or software includes one or more modules corresponding to the above-mentioned functions, and the modules can be is software and/or hardware.

It should be noted that the information exchange, execution process and other contents between the above-mentioned devices/modules are based on the same concept as the method embodiments of the present application, and the specific functions and technical effects brought by them can be found in the method embodiments section for details. It is not repeated here.

The terminal device provided by the embodiment of the present application may include a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the ephemeris forecasting method on the side of the terminal device as described above is implemented. method of any of the examples.

An embodiment of the present application provides a server, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the above-mentioned server-side ephemeris prediction method embodiment is implemented any of the methods.

The embodiment of the present application further provides an ephemeris forecasting system, the system includes a server and a terminal device, wherein the server is configured to implement the method in any one of the above server-side ephemeris forecasting method embodiments. The terminal device is configured to implement the method in any one of the above-mentioned embodiments of the ephemeris prediction method on the side of the terminal device.

Embodiments of the present application further provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the foregoing method embodiments can be implemented.

The embodiments of the present application provide a computer program product, when the computer program product runs on an electronic device, the steps in the foregoing method embodiments can be implemented when the electronic device executes.

An embodiment of the present application further provides a chip system, where the chip system includes a processor, the processor is coupled to a memory, and the processor executes a computer program stored in the memory, so as to implement the methods described in the foregoing method embodiments. method. The chip system may be a single chip, or a chip module composed of multiple chips.

In the foregoing embodiments, the description of each embodiment has its own emphasis. For parts that are not described or described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments. It should be understood that the size of the sequence numbers of the steps in the above embodiments does not mean the sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. Furthermore, in the description of the specification of the present application and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be construed as indicating or implying relative importance. References in this specification to "one embodiment" or "some embodiments" and the like mean that a particular feature, structure or characteristic described in connection with that embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in other embodiments," etc. in various places in this specification are not necessarily All refer to the same embodiment, but mean "one or more but not all embodiments" unless specifically emphasized otherwise.

Finally, it should be noted that: the above are only specific embodiments of the present application, but the protection scope of the present application is not limited to this, and any changes or replacements within the technical scope disclosed in the present application should be covered by the present application. within the scope of protection of the application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims (24)

1. An ephemeris forecasting method, characterized in that being applied to a server, the method comprising:

   Obtain the EOP data of the earth orientation parameters and the historical ephemeris data of the preset period;

   Carry out satellite orbit prediction and satellite clock error prediction according to the EOP data and the historical ephemeris data, and obtain the predicted orbit and the predicted clock difference of a preset time period in the future;

   After fitting the predicted orbit into orbital parameters, encoding the orbital parameters of satellites belonging to the same orbital plane to obtain the orbital parameter encoding of each orbital plane;

   After fitting the predicted clock error into a clock error parameter, encoding the clock error parameter to obtain a clock error parameter encoding;

   Send the ephemeris parameter code to the terminal device, where the ephemeris parameter code includes the clock error parameter code and the orbit parameter code of each orbital plane.
2. The method according to claim 1, wherein the satellite orbit forecast and the satellite clock error forecast are carried out according to the EOP data and the historical ephemeris data, so as to obtain the forecast orbit and the forecast clock difference of a preset time period in the future ,include:

   Perform satellite orbit prediction according to the satellite orbit data and the EOP data to obtain the predicted orbit of the future preset time period;

   Perform clock error forecasting according to satellite clock error data, and obtain the forecast clock error of the future preset time period;

   Wherein, the historical ephemeris data includes the satellite orbit data and the satellite clock error data.
3. The method according to claim 2, wherein the performing satellite orbit prediction according to the satellite orbit data and the EOP data to obtain the predicted orbit of the future preset time period, comprising:

   According to the EOP data, convert the satellite position information under the ground-fixed system in the satellite orbit data into the satellite position information under the inertial system;

   According to the correspondence between the satellite type and the solar light pressure model, determine the target solar light pressure model used by each satellite in the satellite orbit data;

   Based on the target solar light pressure model of each satellite and the satellite position information under the inertial frame, establish the satellite motion equation and variational equation of each satellite;

   Based on the satellite motion equation and the variational equation of each satellite, obtain the reference orbit position and state transition matrix of each satellite at each moment by using a numerical integration method;

   Based on the satellite orbit data, the reference orbit position and the state transition matrix, obtain the satellite orbit state parameters of each satellite at the reference time by means of the least squares overall solution;

   According to the satellite orbit state parameters of each satellite at the reference time and the satellite dynamics model of each satellite, the satellite orbit of each satellite in the future preset time period is obtained by means of numerical integration;

   According to the EOP data, the satellite orbit of each satellite in the future preset time period is converted from the inertial system to the ground-fixed system to obtain the predicted orbit of each satellite in the future preset time period.
4. The method according to claim 3, wherein the correspondence between the satellite type and the solar light pressure model comprises:

   The solar light pressure model corresponding to GPS satellite or GLNOSS satellite is: ECOM5 parameter model;

   The solar light pressure model corresponding to Galileo satellite is: box-wing initial light pressure model and ECOM5 parameter model;

   The solar light pressure models corresponding to Beidou GEO satellites are: initial light pressure model, ECOM5 parameter model and periodic empirical acceleration parameters;

   The solar light pressure models corresponding to the QZSS satellite are: the initial light pressure model and the ECOM5 parameter model.
5. The method according to any one of claims 2 to 4, wherein the satellite orbit data includes precision orbit products for two consecutive days.
6. The method according to any one of claims 2 to 5, wherein the performing clock error prediction according to satellite clock error data to obtain the predicted clock error of the future preset time period, comprising:

   Based on the broadcast ephemeris clock error data, the reference deviation of the precision ephemeris clock error data is corrected, and the corrected precision ephemeris clock error data is obtained, and the satellite clock error data includes the broadcast ephemeris clock error data and the precision satellite clock error data. Calendar clock difference data;

   According to the single-day clock error data in the corrected precise ephemeris clock error data of each satellite, the single-day clock speed of each satellite is obtained by fitting;

   Based on the single-day clock speed of each satellite, obtain the clock speed time series of each satellite;

   According to the clock speed time series of each satellite, the clock speed change rate of each satellite is obtained by fitting;

   According to the initial value of the clock error of each satellite, the clock speed and the rate of change of the clock speed, the predicted clock error of each satellite in the future preset time period is obtained.
7. The method according to claim 6, wherein, based on the broadcast ephemeris clock error data, the reference deviation of the precise ephemeris clock error data is corrected to obtain the corrected precise ephemeris clock error data, comprising:

   Differentiate the broadcast ephemeris clock difference sequence and the precise ephemeris clock difference sequence of the same epoch to obtain the difference sequence of each epoch, wherein the broadcast ephemeris clock difference data includes the broadcast ephemeris of each epoch Clock offset sequence, the precise ephemeris clock offset data includes precise ephemeris clock offset sequence of each epoch;

   determining the mean and standard deviation of each of said series of differences;

   For each difference sequence, remove the difference points that do not meet the preset conditions in the difference sequence according to the average value and the standard deviation, to obtain a target difference sequence;

   Calculate the reference deviation according to the target difference sequence;

   Difference between the precise ephemeris clock error data and the reference deviation is performed to obtain the corrected precise ephemeris clock error data.
8. The method according to claim 7, wherein, according to the average value and the standard deviation, removing the difference points that do not meet a preset condition in the difference sequence to obtain a target difference sequence, comprising: :

   Based on the average value and the standard deviation, determine whether each difference point in the difference sequence satisfies

   

   Among them, x is the difference point in the difference series, μ is the mean value of the difference series, and δ is the standard deviation of the difference series;

   If so, determine that the difference point does not meet the preset condition, remove the difference point that does not meet the preset condition, and obtain a difference sequence after removing the difference point;

   After determining the average value and standard deviation of the difference sequence after excluding the difference point, take the difference sequence after excluding the difference point as the difference sequence, and return the value based on the average value and the standard difference, judge whether each difference point in the difference sequence satisfies the

   

   until no difference point in the difference sequence meets the preset condition, then the difference sequence with no difference point that meets the preset condition is used as the target difference sequence.
9. The method according to any one of claims 6 to 8, wherein, according to the clock speed time series of each satellite, fitting to obtain the clock speed change rate of each satellite, comprising:

   For each satellite, use a sliding window to fit the clock speed time series;

   During the sliding window sliding process, whenever the number of clock speeds in the sliding window is greater than or equal to the preset number, the prediction is made according to the clock speed in the sliding window at the current moment and the fitting result obtained from the previous fitting. the forecast clock speed at the next moment;

   When the difference between the predicted clock speed at the next moment and the clock speed to be added to the sliding window is less than or equal to the first preset threshold, after adding the clock speed to be added to the sliding window to the sliding window, according to The clock speed fitting in the sliding window obtains the current clock speed change rate, and the fitting residual is obtained;

   If the fitting residual is less than or equal to a second preset threshold, the current clock rate change rate is used as the clock rate change rate;

   If the fitting residual is greater than the second preset threshold, reset the sliding window and continue to slide forward until the clock speed time series is traversed or the fitting residual is less than or equal to the second preset threshold;

   When the difference between the clock speed at the next moment and the clock speed to be added to the sliding window is greater than the first preset threshold, reset the sliding window and continue to slide forward until the traversal of the The clock speed time series or the fitting residual is less than or equal to the second preset threshold.
10. An ephemeris forecasting method, characterized in that being applied to a server, the method comprising:

    Obtain EOP data and historical ephemeris data for a preset period;

    Carry out satellite orbit prediction and satellite clock error prediction according to the EOP data and the historical ephemeris data, and obtain the predicted orbit and the predicted clock difference of the future preset time period;

    fitting the forecast clock error to a clock error parameter;

    fitting the predicted orbit to polynomial coefficients using a polynomial model;

    The clock difference parameter and the polynomial coefficient are sent to the terminal device.
11. The method according to claim 10, wherein the fitting the predicted orbit to polynomial coefficients using a polynomial model comprises:

    Sampling the predicted orbits of each satellite at equal intervals to obtain the sampled predicted orbits of each satellite;

    segmenting said sampled forecast orbit for each satellite;

    Fit each segment of the predicted orbit of each satellite according to the basis function and the order of the basis function, determine the basis function coefficients, and use the basis function coefficients as the polynomial coefficients, and the polynomial coefficient model includes the basis function.
12. The method according to claim 10 or 11, wherein the satellite orbit forecast and satellite clock error forecast are performed according to the EOP data and the historical ephemeris data, so as to obtain the forecast orbit and forecast for a preset time period in the future. Clock difference, including:

    Perform satellite orbit prediction according to the satellite orbit data and the EOP data, to obtain the predicted orbit of the future preset time period;

    Perform clock error forecasting according to satellite clock error data, and obtain the forecast clock error of the future preset time period;

    Wherein, the historical ephemeris data includes the satellite orbit data and the satellite clock error data.
13. The method according to claim 12, wherein the performing satellite orbit prediction according to the satellite orbit data and the EOP data to obtain the predicted orbit of the future preset time period, comprising:

    According to the EOP data, convert the satellite position information under the ground-fixed system in the satellite orbit data into the satellite position information under the inertial system;

    According to the correspondence between the satellite type and the solar light pressure model, determine the target solar light pressure model used by each satellite in the satellite orbit data;

    Based on the target solar light pressure model of each satellite and the satellite position information under the inertial frame, establish the satellite motion equation and variational equation of each satellite;

    Based on the satellite motion equation and the variational equation of each satellite, obtain the reference orbit position and state transition matrix of each satellite at each moment by using a numerical integration method;

    Based on the satellite orbit data, the reference orbit position and the state transition matrix, obtain the satellite orbit state parameters of each satellite at the reference time by means of the least squares overall solution;

    According to the satellite orbit state parameters and the satellite dynamics model at the reference time, the satellite orbit of the future preset time period is obtained by means of numerical integration;

    According to the EOP data, the satellite orbit of the future preset time period is converted from the inertial system to the ground-fixed system to obtain the predicted orbit of the future preset time period.
14. The method according to claim 13, wherein the correspondence between the satellite type and the solar light pressure model comprises:

    The solar light pressure model corresponding to GPS satellite or GLNOSS satellite is: ECOM5 parameter model;

    The solar light pressure model corresponding to Galileo satellite is: box-wing initial light pressure model and ECOM5 parameter model;

    The solar light pressure models corresponding to Beidou GEO satellites are: initial light pressure model, ECOM5 parameter model and periodic empirical acceleration parameters;

    The solar light pressure models corresponding to the QZSS satellite are: the initial light pressure model and the ECOM5 parameter model.
15. The method according to any one of claims 12 to 14, wherein the satellite orbit data includes precision orbit products for two consecutive days.
16. The method according to any one of claims 12 to 15, wherein, performing clock error prediction according to satellite clock error data, and obtaining the predicted clock error of the future preset time period, comprising:

    Based on the broadcast ephemeris clock error data, the reference deviation of the precision ephemeris clock error data is corrected, and the corrected precision ephemeris clock error data is obtained, and the satellite clock error data includes the broadcast ephemeris clock error data and the precision satellite clock error data. Calendar clock difference data;

    According to the single-day clock error data in the corrected precise ephemeris clock error data of each satellite, the single-day clock speed of each satellite is obtained by fitting;

    Based on the single-day clock speed of each satellite, obtain the clock speed time series of each satellite;

    According to the clock speed time series of each satellite, the clock speed change rate of each satellite is obtained by fitting;

    According to the initial value of the clock error of each satellite, the clock speed and the rate of change of the clock speed, the predicted clock error of each satellite in the future preset time period is obtained.
17. The method according to claim 16, wherein, based on the broadcast ephemeris clock error data, the reference deviation of the precise ephemeris clock error data is corrected to obtain the corrected precise ephemeris clock error data, comprising:

    Differentiate the broadcast ephemeris clock difference sequence and the precise ephemeris clock difference sequence of the same epoch to obtain the difference sequence of each epoch, wherein the broadcast ephemeris clock difference data includes the broadcast ephemeris of each epoch Clock offset sequence, the precise ephemeris clock offset data includes precise ephemeris clock offset sequence of each epoch;

    determining the mean and standard deviation of each of said series of differences;

    For each difference sequence, remove the difference points that do not meet the preset conditions in the difference sequence according to the average value and the standard deviation, to obtain a target difference sequence;

    Calculate the reference deviation according to the target difference sequence;

    Difference between the precise ephemeris clock error data and the reference deviation is performed to obtain the corrected precise ephemeris clock error data.
18. The method according to claim 17, wherein, according to the average value and the standard deviation, removing the difference value points that do not meet the preset conditions in the difference value sequence to obtain the target difference value sequence, comprising: :

    Based on the average value and the standard deviation, determine whether each difference point in the difference sequence satisfies

    

    Among them, x is the difference point in the difference series, μ is the mean value of the difference series, and δ is the standard deviation of the difference series;

    If so, determine that the difference point does not meet the preset condition, remove the difference point that does not meet the preset condition, and obtain a difference sequence after removing the difference point;

    After determining the average value and standard deviation of the difference sequence after excluding the difference point, take the difference sequence after excluding the difference point as the difference sequence, and return the value based on the average value and the standard difference, judge whether each difference point in the difference sequence satisfies the

    

    until no difference point in the difference sequence meets the preset condition, then the difference sequence with no difference point that meets the preset condition is used as the target difference sequence.
19. The method according to any one of claims 16 to 18, wherein, according to the clock speed time series of each satellite, fitting to obtain the clock speed change rate of each satellite, comprising:

    For each satellite, use a sliding window to fit the clock speed time series;

    During the sliding window sliding process, whenever the number of clock speeds in the sliding window is greater than or equal to the preset number, the prediction is made according to the clock speed in the sliding window at the current moment and the fitting result obtained from the previous fitting. the forecast clock speed at the next moment;

    When the difference between the predicted clock speed at the next moment and the clock speed to be added to the sliding window is less than or equal to the first preset threshold, after adding the clock speed to be added to the sliding window to the sliding window, according to The clock speed fitting in the sliding window obtains the current clock speed change rate, and the fitting residual is obtained;

    If the fitting residual is less than or equal to a second preset threshold, the current clock rate change rate is used as the clock rate change rate;

    If the fitting residual is greater than the second preset threshold, reset the sliding window and continue to slide forward until the clock speed time series is traversed or the fitting residual is less than or equal to the first preset threshold;

    When the difference between the clock speed at the next moment and the clock speed to be added to the sliding window is greater than the first preset threshold, reset the sliding window and continue to slide forward until the traversal of the The clock speed time series or the fitting residual is less than or equal to the first preset threshold.
20. A method for ephemeris forecasting, characterized in that it is applied to terminal equipment, the method comprising:

    Receive polynomial coefficients and clock error parameters from the server;

    determining the positions and velocities of visible GNSS satellites based on the polynomial coefficients;

    determining the clock error of the visible GNSS satellite according to the clock error parameter;

    Based on the position, velocity and clock offset of the visible GNSS satellites, the current position and/or velocity is determined.
21. The method according to claim 20, wherein the determining the position and velocity of the visible GNSS satellites according to the polynomial coefficients comprises:

    determining the position of the visible GNSS satellite according to the polynomial coefficients and the basis function of the polynomial model;

    The velocity of the visible GNSS satellite is determined from the polynomial coefficients and the basis function derivatives of the polynomial model.
22. A server, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, when the processor executes the computer program, the implementation of claims 1 to 9 Or the method of any one of 10 to 19.
23. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, when the processor executes the computer program, the process according to claim 20 to The method of any one of 21.
24. A computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, any one of claims 1 to 9 or 10 to 19 or 20 to 21 is implemented method described in item.

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