CN119738853A

CN119738853A - A smart phone positioning method, system, device and medium

Info

Publication number: CN119738853A
Application number: CN202410540395.8A
Authority: CN
Inventors: 李婉清; 宋江波
Original assignee: Sun Yat Sen University
Current assignee: Sun Yat Sen University
Priority date: 2024-04-30
Filing date: 2024-04-30
Publication date: 2025-04-01
Anticipated expiration: 2044-04-30
Also published as: CN119738853B

Abstract

The present invention discloses a smart phone positioning method, system, device and medium, and relates to the technical field of Internet of Things. Based on the global navigation satellite system data, the carrier-to-noise ratio average value and the pseudo-range multipath error average value are calculated respectively to generate the carrier-to-noise ratio average value and the pseudo-range multipath error average value. The environmental scene is identified according to the carrier-to-noise ratio average value and the pseudo-range multipath error average value. When the scene type is a multipath severe scene, the barometric altimeter data and the global navigation satellite system data are used according to the tight combination fusion strategy to perform data fusion positioning to generate the first positioning data. When the scene type is an open environment scene, data fusion positioning is performed based on the carrier-to-noise ratio average value, the pseudo-range multipath error average value, the barometric altimeter data and the global navigation satellite system data to generate the second positioning data. The global navigation satellite system data and the barometric altimeter data are used for data fusion positioning, which improves the accuracy and robustness of the smart phone in obtaining global location information.

Description

Smart phone positioning method, system, equipment and medium

Technical Field

The invention relates to the technical field of Internet of things, in particular to a smart phone positioning method, a smart phone positioning system, smart phone positioning equipment and a smart phone positioning medium.

Background

The smart phone is an important terminal device in smart city and everything interconnection, the high-precision positioning technology based on the smart phone has very wide application market, satellite navigation and position service have important space-time energized functions, so that powerful vitality is provided for digital economic development in China, and great effect is also exerted in daily life production of people.

The smart phone is provided with a plurality of sensors, so that the research on how to use various sensors for fusion positioning on the basis of not changing the original hardware setting has important theoretical and application values. However, the existing smart phone only uses a single sensor to perform positioning, so that the robustness is low, and the satellite navigation elevation measurement is not accurate enough, so that the accuracy of a positioning result is low.

Disclosure of Invention

The invention provides a smart phone positioning method, a system, equipment and a medium, which solve the technical problems that the existing smart phone only uses a single sensor to perform positioning, the robustness is low, the satellite navigation elevation measurement is not accurate enough, and the accuracy of a positioning result is low.

The invention provides a smart phone positioning method, which comprises the following steps:

acquiring global navigation satellite system data and barometric altitude data acquired by a smart phone, and respectively calculating a carrier-to-noise ratio average value and a pseudo-range multipath error average value based on the global navigation satellite system data to generate the carrier-to-noise ratio average value and the pseudo-range multipath error average value;

performing environment scene recognition according to the carrier-to-noise ratio average value and the pseudo-range multipath error average value, and determining a scene type;

when the scene type is a multipath severe scene, carrying out data fusion positioning by adopting the barometric altimeter data and the global navigation satellite system data according to a tightly combined fusion strategy to generate first positioning data corresponding to the smart phone;

and when the scene type is an open environment scene, performing data fusion positioning based on the carrier-to-noise ratio average value, the pseudo-range multipath error average value, the barometric altimeter data and the global navigation satellite system data to generate second positioning data corresponding to the smart phone.

Optionally, the global navigation satellite system data includes a plurality of satellite signal wavelengths, a plurality of ionospheric errors, a plurality of satellite signal powers, a plurality of bilateral noise power spectral densities, a plurality of pseudo-range values, a plurality of carrier phase values and a plurality of carrier phase ambiguities, and the step of calculating a carrier-to-noise ratio average value and a pseudo-range multipath error average value based on the global navigation satellite system data respectively to generate a carrier-to-noise ratio average value and a pseudo-range multipath error average value includes:

calculating the ratio between the satellite signal power and the corresponding bilateral noise power spectral density respectively to generate a plurality of carrier-to-noise ratios;

calculating the sum value among all the carrier-to-noise ratios to generate a carrier-to-noise ratio sum value;

Calculating the ratio between the carrier-to-noise ratio sum value and the corresponding carrier-to-noise ratio number to generate a carrier-to-noise ratio average value;

Substituting the satellite signal wavelength, the ionosphere error, the pseudo-range value, the carrier phase value and the carrier phase ambiguity into a preset pseudo-range multipath error expression for calculation to generate a plurality of pseudo-range multipath errors;

The preset pseudo-range multipath error expression is:

mp=ρ-Φ·λ-2I+N_iλ;

Wherein mp is a pseudo-range multipath error, ρ is a pseudo-range value observed by the smart phone, Φ is a carrier phase value observed by the smart phone, λ is a satellite signal wavelength, I is an ionospheric error, and N _i is a carrier phase ambiguity;

calculating the sum value among all the pseudo-range multipath errors to generate a pseudo-range multipath error sum value;

and calculating the ratio between the pseudo-range multipath error sum value and the corresponding error quantity, and generating a pseudo-range multipath error average value.

Optionally, the step of identifying the environmental scene according to the average value of the carrier-to-noise ratio and the average value of the multipath error of the pseudo range and determining the scene type includes:

when the average value of the carrier-to-noise ratio is smaller than a first preset value and the average value of the pseudo-range multipath errors is larger than a second preset value, the scene type is a multipath severe scene;

and when the average value of the carrier-to-noise ratio is larger than or equal to the first preset value and the average value of the pseudo-range multipath errors is smaller than the second preset value, the scene type is an open environment scene.

Optionally, when the scene type is a multipath severe scene, performing data fusion positioning by using the barometric altimeter data and the global navigation satellite system data according to a tightly combined fusion policy, and generating first positioning data corresponding to the smart phone, where the step includes:

When the scene type is a multipath severe scene, a quaternary nonlinear equation is constructed by adopting a satellite three-dimensional position, a smart phone three-dimensional position, a pseudo-range observation correction value and a smart phone clock difference in the global navigation satellite system data;

linearizing the quaternary nonlinear equation to generate a linearization positioning matrix equation;

and carrying out iterative solution on the linearization positioning matrix equation by adopting a Newton iteration method, and determining first positioning data corresponding to the smart phone.

Optionally, the step of iteratively solving the linearized positioning matrix equation by using a newton iteration method to determine first positioning data corresponding to the smart phone includes:

Updating the initial smart phone position coordinates and the initial clock difference value corresponding to the positioning equation by adopting a preset updating formula, generating intermediate smart phone position coordinates and intermediate clock difference values, and counting iteration times;

the preset updating formula is as follows:

δt_u,k＝δt_u,k-1+Δδt_u;

Wherein S _k is the position coordinate of the smart phone after the k iteration update, S _k-1 is the position coordinate of the smart phone after the k-1 iteration update, deltaS= [ Deltax, deltay, deltaz ] represents the three-dimensional position change amount in the solution of the positioning equation, deltat _u,k is the clock difference of the smart phone estimated by the k iteration, deltat _u,k-1 is the clock difference of the smart phone estimated by the k-1 iteration, and Deltat _u is the estimated clock difference change amount of the smart phone;

When the iteration times are smaller than or equal to preset iteration times, substituting the position coordinates of the middle intelligent mobile phone and the middle Zhong Chazhi into a preset precision calculation formula to generate a precision value;

The preset precision calculation formula is as follows:

Wherein A is an accuracy value; Δs= [ Δx, Δy, Δz ] represents the three-dimensional position change amount in the positioning equation solution, the symbols ||·| represent a two-norm function; delta t _u is the estimated change of the clock skew of the smart phone;

When the precision value is smaller than a preset threshold value, the intermediate smart phone position coordinate and the intermediate Zhong Chazhi are used as a target smart phone position coordinate and a target Zhong Chazhi;

coupling the altitude variation in the barometric altimeter data, the target smart phone position coordinate and the target Zhong Chazhi to a preset positioning equation to carry out least square solution to obtain first positioning data corresponding to the smart phone;

The preset positioning equation is as follows:

Wherein (phi, lambda, h) is a geodetic coordinate system coordinate corresponding to a geodetic fixed coordinate system coordinate (x, y, z), h is a third coordinate dimension of the geodetic coordinate system coordinate, called geodetic altitude, phi is geodetic latitude, lambda is geodetic longitude, delta t _u is an estimated change in clock of the smart phone, delta h _p is a change in altitude, R is a radius of curvature of a mortise unitary circle of a reference ellipsoid, e is a sphere eccentricity, a is a long radius of the reference sphere, d is a short radius, and p is an intermediate variable;

And when the precision value is larger than a preset threshold value, taking the intermediate intelligent mobile phone position coordinate and the intermediate Zhong Chazhi as a new initial intelligent mobile phone position coordinate and a new initial Zhong Chazhi, skipping to execute the steps of updating the initial intelligent mobile phone position coordinate and the initial clock difference value corresponding to the positioning equation by adopting a preset updating formula, generating an intermediate intelligent mobile phone position coordinate and an intermediate clock difference value, and counting the iteration times.

Optionally, the step of generating the second positioning data corresponding to the smart phone based on the carrier-to-noise ratio average value, the pseudo-range multipath error average value, the barometric altimeter data and the global navigation satellite system data for data fusion positioning includes:

When the pseudo-range multipath error average value is smaller than a third preset value and the carrier-to-noise ratio average value is larger than or equal to a fourth preset value, carrying out data fusion positioning by adopting the barometric altimeter data and the global navigation satellite system data according to a loose combination fusion strategy to generate first three-dimensional positioning data corresponding to the smart phone;

And when the pseudo-range multipath error average value is in a first preset interval and the carrier-to-noise ratio average value is in a second preset interval, carrying out data fusion positioning by adopting the barometric altimeter data and the global navigation satellite system data according to the tight combination fusion strategy, and generating second three-dimensional positioning data corresponding to the smart phone.

Optionally, the step of performing data fusion positioning by using the barometric altimeter data and the global navigation satellite system data according to a loose combination fusion policy to generate first three-dimensional positioning data corresponding to the smart phone includes:

inputting the barometric altimeter data and the global navigation satellite system data into a preset Kalman filtering model to perform state variable estimation, and generating an estimated state vector;

substituting the gain matrix, the measurement matrix and the estimated state vector corresponding to the estimated state vector into a preset state variable estimation formula, and calculating to obtain a Kalman filtering state quantity estimated value;

the preset state variable estimation formula is as follows:

Wherein, The state quantity estimation value is Kalman filtering state quantity estimation value; For the estimated state vector from time f-1 to time f, K _f is the gain matrix, Z _f is the measurement matrix, H _f is the measurement matrix, and the expression is set as [0, 1] ^T;

Subtracting the initial altitude data in the Kalman filtering state quantity estimation value from the altitude data in the global navigation satellite system data to generate target altitude data;

and updating coordinate data corresponding to the Kalman filtering state quantity estimation value by adopting the target height data to generate first three-dimensional positioning data corresponding to the smart phone.

The invention also provides a smart phone positioning system, which comprises:

The average value generation module is used for acquiring global navigation satellite system data and barometric altitude data acquired by the smart phone, respectively calculating a carrier-to-noise ratio average value and a pseudo-range multipath error average value based on the global navigation satellite system data, and generating the carrier-to-noise ratio average value and the pseudo-range multipath error average value;

the scene type determining module is used for carrying out environment scene recognition according to the carrier-to-noise ratio average value and the pseudo-range multipath error average value and determining the scene type;

The first positioning data generation module is used for carrying out data fusion positioning by adopting the barometric altimeter data and the global navigation satellite system data according to a tight combination fusion strategy when the scene type is a multipath severe scene, so as to generate first positioning data corresponding to the smart phone;

And the second positioning data generation module is used for carrying out data fusion positioning based on the carrier-to-noise ratio average value, the pseudo-range multipath error average value, the barometric altimeter data and the global navigation satellite system data when the scene type is an open environment scene, and generating second positioning data corresponding to the smart phone.

The invention also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the computer program when executed by the processor causes the processor to execute the steps for realizing the positioning method of any one of the smart phones.

The present invention also provides a computer readable storage medium having stored thereon a computer program which when executed implements a smartphone positioning method as any one of the above.

From the above technical scheme, the invention has the following advantages:

According to the invention, the carrier-to-noise ratio average value and the pseudo-range multipath error average value are respectively calculated based on the global navigation satellite system data by acquiring the global navigation satellite system data and the barometric altitude data acquired by the smart phone, so that the carrier-to-noise ratio average value and the pseudo-range multipath error average value are generated. And carrying out environmental scene recognition based on the carrier-to-noise ratio average value and the pseudo-range multipath error average value, and determining the scene type. When the scene type is a multipath severe scene, data fusion positioning is carried out by adopting the barometric altimeter data and the global navigation satellite system data according to a tightly combined fusion strategy, so as to generate first positioning data corresponding to the smart phone. And when the scene type is an open environment scene, carrying out data fusion positioning based on the carrier-to-noise ratio average value, the pseudo-range multipath error average value, the barometric altimeter data and the global navigation satellite system data, and generating second positioning data corresponding to the smart phone. The intelligent mobile phone positioning method and system based on the sensor detection solves the technical problems that an existing intelligent mobile phone only uses a single sensor to perform positioning robustness is low, satellite navigation elevation measurement is not accurate enough, and positioning result accuracy is low. The global navigation satellite system data and the barometric altitude data are adopted for data fusion positioning, so that the accuracy and the robustness of acquiring global position information by the smart phone are improved, and the accuracy of an obtained positioning result is high.

Drawings

In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained from these drawings without inventive faculty for a person skilled in the art.

Fig. 1 is a flowchart of steps of a smart phone positioning method according to a first embodiment of the present invention;

fig. 2 is a flowchart of steps of a smart phone positioning method according to a second embodiment of the present invention;

fig. 3 is a block diagram of a smart phone positioning system according to a third embodiment of the present invention;

fig. 4 is a block diagram of an electronic device according to a fourth embodiment of the present invention.

Detailed Description

The embodiment of the invention provides a smart phone positioning method, a system, equipment and a medium, which are used for solving the technical problems that the positioning robustness of the existing smart phone is low by only using a single sensor, and the satellite navigation elevation measurement is not accurate enough, so that the accuracy of a positioning result is low.

In order to make the objects, features and advantages of the present invention more comprehensible, the technical solutions in the embodiments of the present invention are described in detail below with reference to the accompanying drawings, and it is apparent that the embodiments described below are only some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Referring to fig. 1, fig. 1 is a flowchart illustrating steps of a smart phone positioning method according to an embodiment of the invention.

The first embodiment of the invention provides a smart phone positioning method, which comprises the following steps:

Step 101, acquiring global navigation satellite system data and barometric altitude data acquired by a smart phone, and respectively calculating a carrier-to-noise ratio average value and a pseudo-range multipath error average value based on the global navigation satellite system data to generate the carrier-to-noise ratio average value and the pseudo-range multipath error average value.

In the embodiment of the invention, the smart phone is provided with two sensors of a Global Navigation Satellite System (GNSS) and an barometer altimeter (PS). And acquiring global navigation satellite system data and barometric altitude data acquired by the smart phone. The global navigation satellite system data includes a plurality of satellite signal wavelengths, a plurality of ionospheric errors, a plurality of satellite signal powers, a plurality of bilateral noise power spectral densities, a plurality of pseudo-range values, a plurality of carrier phase ambiguities, and the like, which are used for realizing positioning calculation. And respectively calculating the ratio between the satellite signal power and the corresponding bilateral noise power spectral density to obtain a plurality of carrier-to-noise ratios. And calculating the sum value among all the carrier-to-noise ratios, and generating the carrier-to-noise ratio sum value. And calculating the ratio between the sum of the carrier-to-noise ratios and the corresponding carrier-to-noise ratio number, and generating a carrier-to-noise ratio average value. And substituting the satellite signal wavelength, the ionosphere error, the pseudo-range value, the carrier phase value and the carrier phase ambiguity into a preset pseudo-range multipath error expression for calculation to generate a plurality of pseudo-range multipath errors. And calculating the sum value among all the pseudo-range multipath errors, and generating the sum value of the pseudo-range multipath errors. And calculating the ratio between the sum value of the pseudo-range multipath errors and the corresponding error quantity, and generating a pseudo-range multipath error average value.

And 102, carrying out environmental scene recognition according to the carrier-to-noise ratio average value and the pseudo-range multipath error average value, and determining the scene type.

In the embodiment of the invention, the carrier-to-noise ratio average value and the pseudo-range multipath error average value are respectively compared with a first preset value and a second preset value. When the average value of the carrier-to-noise ratio is smaller than a first preset value and the average value of the pseudo-range multipath errors is larger than a second preset value, the scene type is a multipath serious scene. When the average value of the carrier-to-noise ratio is larger than or equal to a first preset value and the average value of the pseudo-range multipath errors is smaller than a second preset value, the scene type is an open environment scene.

And 103, when the scene type is a multipath severe scene, carrying out data fusion positioning by adopting the barometric altimeter data and the global navigation satellite system data according to a tightly combined fusion strategy, and generating first positioning data corresponding to the smart phone.

In the embodiment of the invention, when the scene type is a multipath severe scene, a quaternary nonlinear equation is constructed by adopting a satellite three-dimensional position, a smart phone three-dimensional position, a pseudo-range observation correction value and a smart phone clock difference in global navigation satellite system data. And linearizing the quaternary nonlinear equation to generate a linearization positioning matrix equation. And carrying out iterative solution on the linear positioning matrix equation by adopting a Newton iteration method, and determining first positioning data corresponding to the smart phone.

And 104, when the scene type is an open environment scene, performing data fusion positioning based on the carrier-to-noise ratio average value, the pseudo-range multipath error average value, the barometric altimeter data and the global navigation satellite system data to generate second positioning data corresponding to the smart phone.

In the embodiment of the invention, when the average value of the pseudo-range multipath errors is smaller than a third preset value and the average value of the carrier-to-noise ratio is larger than or equal to a fourth preset value, data fusion positioning is performed by adopting the barometric altimeter data and the global navigation satellite system data according to a loose combination fusion strategy, and first three-dimensional positioning data corresponding to the smart phone is generated. When the pseudo-range multipath error average value is in a first preset interval and the carrier-to-noise ratio average value is in a second preset interval, carrying out data fusion positioning by adopting the barometric altimeter data and the global navigation satellite system data according to a tight combination fusion strategy, and generating second three-dimensional positioning data corresponding to the smart phone.

In the embodiment of the invention, the carrier-to-noise ratio average value and the pseudo-range multipath error average value are generated by acquiring the global navigation satellite system data and the barometric altitude data acquired by the smart phone and respectively calculating the carrier-to-noise ratio average value and the pseudo-range multipath error average value based on the global navigation satellite system data. And carrying out environmental scene recognition based on the carrier-to-noise ratio average value and the pseudo-range multipath error average value, and determining the scene type. When the scene type is a multipath severe scene, data fusion positioning is carried out by adopting the barometric altimeter data and the global navigation satellite system data according to a tightly combined fusion strategy, so as to generate first positioning data corresponding to the smart phone. And when the scene type is an open environment scene, carrying out data fusion positioning based on the carrier-to-noise ratio average value, the pseudo-range multipath error average value, the barometric altimeter data and the global navigation satellite system data, and generating second positioning data corresponding to the smart phone. The intelligent mobile phone positioning method and system based on the sensor detection solves the technical problems that an existing intelligent mobile phone only uses a single sensor to perform positioning robustness is low, satellite navigation elevation measurement is not accurate enough, and positioning result accuracy is low. The global navigation satellite system data and the barometric altitude data are adopted for data fusion positioning, so that the accuracy and the robustness of acquiring global position information by the smart phone are improved, and the accuracy of an obtained positioning result is high.

Referring to fig. 2, fig. 2 is a flowchart illustrating steps of a smart phone positioning method according to a second embodiment of the present invention.

The second embodiment of the invention provides another smart phone positioning method, which comprises the following steps:

Step 201, acquiring global navigation satellite system data and barometric altitude data acquired by a smart phone, and respectively calculating a carrier-to-noise ratio average value and a pseudo-range multipath error average value based on the global navigation satellite system data to generate the carrier-to-noise ratio average value and the pseudo-range multipath error average value.

Further, the global navigation satellite system data includes a plurality of satellite signal wavelengths, a plurality of ionospheric errors, a plurality of satellite signal powers, a plurality of bilateral noise power spectral densities, a plurality of pseudorange values, a plurality of carrier phase values, and a plurality of carrier phase ambiguities. Step 201 may include the following sub-steps S11-S16:

s11, calculating the ratio between the satellite signal power and the corresponding bilateral noise power spectral density respectively, and generating a plurality of carrier-to-noise ratios.

S12, calculating the sum value among all the carrier-to-noise ratios, and generating the carrier-to-noise ratio sum value.

S13, calculating the ratio between the carrier-to-noise ratio sum value and the corresponding carrier-to-noise ratio number, and generating a carrier-to-noise ratio average value.

S14, substituting the satellite signal wavelength, the ionosphere error, the pseudo-range value, the carrier phase value and the carrier phase ambiguity into a preset pseudo-range multipath error expression for calculation to generate a plurality of pseudo-range multipath errors.

S15, calculating the sum value of all the pseudo-range multipath errors, and generating the pseudo-range multipath error sum value.

S16, calculating the ratio between the sum value of the pseudo-range multipath errors and the corresponding error quantity, and generating a pseudo-range multipath error average value.

In the embodiment of the invention, the carrier-to-noise ratio is the original observed quantity obtained after the GNSS chip processes the signals. The pseudorange multipath error may be calculated using the code subtracted phase as a combination. The specific calculation process is as follows:

The carrier to noise ratio expression is:

Wherein, C/N ₀ is the carrier-to-noise ratio, the unit is dB-Hz, P _R is satellite signal power, and N ₀ is bilateral noise power spectral density.

The preset pseudo-range multipath error expression is:

mp=ρ-Φ·λ-2I+N_iλ;

Wherein mp is a pseudo-range multipath error, ρ is a pseudo-range value observed by the smart phone, Φ is a carrier phase value observed by the smart phone, λ is a satellite signal wavelength, I is an ionospheric error, N _i is a carrier phase ambiguity, which can be solved by an LAMBDA algorithm or regarded as a constant under a static condition, and the constant can be eliminated after the mp is subjected to a moving average process.

And respectively calculating the ratio between the satellite signal power and the corresponding bilateral noise power spectral density through the carrier-to-noise ratio expression to obtain a plurality of carrier-to-noise ratios. And then calculating the sum value among all the carrier-to-noise ratios to obtain the carrier-to-noise ratio sum value. And finally, calculating the ratio between the sum of the carrier-to-noise ratios and the corresponding carrier-to-noise ratio number to obtain the average value of the carrier-to-noise ratios. And substituting the satellite signal wavelength, the ionosphere error, the pseudo range value, the carrier phase value and the carrier phase ambiguity acquired by the smart phone into a preset pseudo range multipath error expression, and calculating to obtain a plurality of pseudo range multipath errors. And then obtaining the sum value of the pseudo-range multipath errors by calculating the sum value among all the pseudo-range multipath errors. And finally, calculating the ratio between the sum of the pseudo-range multipath errors and the corresponding error quantity to obtain the average value of the pseudo-range multipath errors.

And 202, carrying out environmental scene recognition according to the average value of the carrier-to-noise ratio and the average value of the pseudo-range multipath error, and determining the scene type.

Further, step 202 may comprise the following sub-steps S21-S22:

S21, when the average value of the carrier-to-noise ratio is smaller than a first preset value and the average value of the pseudo-range multipath errors is larger than a second preset value, the scene type is a multipath serious scene.

S22, when the average value of the carrier-to-noise ratio is larger than or equal to a first preset value and the average value of the pseudo-range multipath errors is smaller than a second preset value, the scene type is an open environment scene.

The first preset value is 30dB-Hz. The second preset value is 5m.

In the embodiment of the invention, the scenes where the smart phone is positioned are classified into severe multipath scenes and open environment scenes according to the carrier-to-noise ratio and the pseudo-range multipath errors. The method specifically comprises the step of calculating a carrier-to-noise ratio average value smaller than 30dB-Hz and a pseudo-range multipath error average value larger than 5m, wherein the scene type corresponding to the smart phone is a multipath severe scene. When the average value of the calculated carrier-to-noise ratio is larger than or equal to 30dB-Hz and the average value of the pseudo-range multipath errors is smaller than 5m, the scene type corresponding to the intelligent mobile phone is an open environment scene.

And 203, when the scene type is a multipath severe scene, constructing a quaternary nonlinear equation by adopting a satellite three-dimensional position, a smart phone three-dimensional position, a pseudo-range observation correction value and a smart phone clock difference in the global navigation satellite system data.

In the embodiment of the invention, different fusion strategies are adopted in different scenes. The fusion strategy is divided into a loose combination fusion strategy and a tight combination fusion strategy. The tight combination fusion strategy is applicable to multipath severe scenes and open environment scenes, while the loose combination fusion strategy is applicable to open environment scenes. And adopting tightly combined data fusion in a multipath severe scene. The tightly combined fusion strategy is to fuse the altitude information of the altimeter into a GNSS positioning algorithm by utilizing the characteristic of high accuracy of the altitude of the altimeter, and the tightly combined fusion strategy comprises the following specific contents:

Obtaining a relation between the following positions and distances according to the position relation, namely adopting a satellite three-dimensional position, a smart phone three-dimensional position, a pseudo-range observation correction value and a smart phone clock difference in global navigation satellite system data to construct a quaternary nonlinear equation, wherein the quaternary nonlinear equation is as follows:

Wherein (x ⁽ⁿ⁾,y⁽ⁿ⁾,z⁽ⁿ⁾) is the satellite three-dimensional position of the nth satellite, N represents the nth satellite observed by the smartphone, n=1, 2,. Where N is the temporary number of the satellite or the satellite measurement value, (x ⁽¹⁾,y⁽¹⁾,z⁽¹⁾) is the satellite three-dimensional position of the 1 st satellite, (x ⁽²⁾,y⁽²⁾,z⁽²⁾) is the satellite three-dimensional position of the 2 nd satellite, (x ^(N),y^(N),z^(N)) is the satellite three-dimensional position of the nth satellite, (x, y, z) is the smartphone three-dimensional position, δt _u is the smartphone clock error, n=1, 2,. Where N is the temporary number of the satellite or the satellite measurement value; a modified pseudorange observation for satellite 1; A pseudo-range observation value corrected for the 2 nd satellite; and the corrected pseudo-range observation value is obtained by deducting satellite clock errors (which are sent to the mobile phone to be stored in a navigation message file by satellites), ionosphere errors (estimated by using a Klobuchar model) and troposphere errors (estimated by using a Saastamoinen model) from the pseudo-range observation value obtained by processing a GNSS chip in the smart phone.

And 204, linearizing the quaternary nonlinear equation to generate a linearization positioning matrix equation.

In the embodiment of the invention, the four-element nonlinear equation is linearized to obtain a linearized positioning matrix equation, wherein the linearized positioning matrix equation is as follows:

The geometric matrix G is a jacobian matrix and is only related to the geometric position of each satellite relative to the smart phone, wherein (delta x, delta y, delta z, delta t _u) in [ delta x, delta y, delta z ] is the coordinate variation of the smart phone under a geocentric fixed coordinate system of the three directions of the position xyz between two adjacent observation moments, delta tu is the estimated clock difference variation of the smart phone, b is a residual matrix, r is the distance from the smart phone to the satellite, n=1, 2, N is the temporary number of the satellite or the satellite measurement value, k represents the number of newton iterations in progress of the current epoch, namely k-1 is the number of iterations already completed in the current epoch, and k=1 represents the first iteration; r ^(N)(S_k-1 for the nth smartphone-to-satellite distance) versus x; r ^(N)(S_k-1) a value at k-1 for the bias of y; r ^(N)(S_k-1) value of the partial derivative of z at k-1, [ I ^(N)(S_k-1)]^T is S _k-1＝(x_k-1,y_k-1,z_k-1) is the position coordinate of the smart phone at the k-1 position; The corrected pseudo-range observation value for the nth satellite, r ^(N)(S_k-1) the geometric distance from the receiver to the nth satellite, and δt _u,k-1 the clock difference of the smart phone estimated by the kth-1 iteration.

And 205, carrying out iterative solution on the linearized positioning matrix equation by adopting a Newton iteration method, and determining first positioning data corresponding to the smart phone.

In the embodiment of the invention, linearization is needed to solve the quaternary nonlinear equation, so that the linearized positioning matrix equation is obtained. And in order to obtain a solution, calculating by adopting a Newton iteration method to obtain a positioning equation solution of a positioning linearization positioning matrix equation.

Further, step 205 may include the following substeps S31-S35:

And S31, updating the initial smart phone position coordinates and the initial clock difference value corresponding to the positioning equation by adopting a preset updating formula, generating intermediate smart phone position coordinates and intermediate clock difference values, and counting the iteration times.

S32, substituting the position coordinates of the middle intelligent mobile phone and the middle clock difference value into a preset precision calculation formula to generate a precision value when the iteration times are smaller than or equal to the preset iteration times.

And S33, when the precision value is smaller than a preset threshold value, taking the middle intelligent mobile phone position coordinate and the middle Zhong Chazhi as the target intelligent mobile phone position coordinate and the target clock difference value.

And S34, coupling the altitude variation in the barometric altimeter data, the target smart phone position coordinates and the target Zhong Chazhi into a preset positioning equation to carry out least square solution, and obtaining first positioning data corresponding to the smart phone.

And S35, when the precision value is larger than a preset threshold value, taking the middle intelligent mobile phone position coordinate and the middle Zhong Chazhi as a new initial intelligent mobile phone position coordinate and a new initial Zhong Chazhi, skipping to execute the steps of updating the initial intelligent mobile phone position coordinate and the initial clock difference value corresponding to the positioning equation by adopting a preset updating formula, generating a middle intelligent mobile phone position coordinate and a middle clock difference value, and counting the iteration times.

In the embodiment of the invention, pseudo ranges, satellite clock differences and satellite positions of all satellites acquired by a GNSS chip in the smart phone relative to the smart phone are used as inputs, wherein the coordinate variation and the clock difference variation are solutions in Newton iterative calculation, and finally the obtained solutions are three-dimensional position coordinates of the smart phone, the clock differences of the smart phone, namely the position coordinates of the target smart phone and the target clock difference. The specific solving process is as follows:

Given an initial value For a given smartphone location initial value δt _u,0 is a given smartphone clock difference initial value. For the kth solving, the positioning equation obtained by solving the iterative format based on the Newton iteration method is solved as follows:

The geometric matrix G is a jacobian matrix and is only related to the geometric position of each satellite relative to the smart phone, G ^T is a transpose of G, (. Cndot.) ^-1 is the inverse of the matrix, (Deltax, deltay, deltaz) is the coordinate variation of the smart phone under a geocentric fixed coordinate system in three directions of the position xyz between two adjacent observation moments (two adjacent iterations), x _k,y_k,z_k is the coordinate of the smart phone under the geocentric fixed coordinate system in the kth iteration, (x _k-1,y_k-1,z_k-1) is the coordinate of the smart phone under the geocentric fixed coordinate system in the kth-1 iteration, deltat _u is the estimated smart phone clock difference variation, deltat _u,k is the smart phone clock difference estimated in the kth iteration, deltat _u,k-1 is the smart phone clock difference estimated in the kth-1 iteration, and b is the residual matrix.

The height change amount added to the barometric altimeter data, the height change amount Δu of the user is represented by the height change amount Δh _p of the barometric altimeter, Δu=Δh _p.

Therefore, after the height variation is added, the positioning equation innovated by the invention, namely the preset positioning equation is obtained as follows:

The method comprises the steps of determining a clock difference change amount of a smart phone, wherein delta t _u is an estimated clock difference change amount of the smart phone, delta h _p is a height change amount, phi, lambda, h is a geodetic coordinate system coordinate, and a certain conversion relation exists corresponding to a geodetic fixed coordinate system coordinate (x, y, z), wherein a conversion formula is as follows:

Wherein R is the curvature radius of a mortise unitary circle of a reference ellipsoid, e is the eccentricity of the sphere, a is the long radius of the reference sphere, d is the short radius, p is the third dimensional coordinate of the coordinates (phi, lambda, h) of the geodetic coordinate system, h is the geodetic height, phi is the geodetic dimension, and lambda is the geodetic longitude.

And carrying out iterative solution through linearization of the positioning matrix equation and a preset positioning equation to obtain a final solution result, namely obtaining solutions [ delta x, delta y, delta z, delta t _u]^T, wherein the altitude data is added into the positioning equation for positioning solution.

Updating the initial smart phone position coordinate and the initial clock difference value corresponding to the positioning equation by adopting a preset updating formula, generating an intermediate smart phone position coordinate and an intermediate clock difference value, and counting the iteration times to obtain a smart phone position coordinate S _k and a clock difference value delta t _u,k after the following k iteration updating, namely the preset updating formula is as follows:

δt_u,k＝δt_u,k-1+Δδt_u;

The method comprises the steps of S _k, S _k-1, delta S= [ delta x, delta y, delta z ] and delta t _u,k, wherein S _k is the position coordinate of the smart phone after the k iteration update, S _k-1 is the position coordinate of the smart phone after the k-1 iteration update, delta S= [ delta x, delta y, delta z ] represents the three-dimensional position change in the solution of the positioning equation, delta t _u,k is the smart phone clock difference estimated by the k iteration, delta t _u,k-1 is the smart phone clock difference estimated by the k-1 iteration, and delta t _u is the estimated smart phone clock difference change.

And substituting the position coordinates of the middle intelligent mobile phone and the middle clock difference value into a preset precision calculation formula to generate a precision value when the iteration times are smaller than or equal to the preset iteration times. The preset accuracy calculation formula, namely the accuracy requirement judgment basis isWhere Δs= [ Δx, Δy, Δz) represents the three-dimensional position change amount in the positioning equation solution, the symbols ||·| represent a function of the two norms, delta t _u is expressed as the smart phone clock bias solved in the positioning equation solution. JudgingIf the position coordinate of the middle smart phone is smaller than the preset threshold value, the solution meets the precision requirement, and the position coordinate of the middle smart phone and the middle Zhong Chazhi are used as the position coordinate of the target smart phone and the difference value of the target clock. And coupling the altitude variation in the barometric altimeter data, the target smart phone position coordinate and the target Zhong Chazhi to a preset positioning equation to solve least square, so as to obtain first positioning data corresponding to the smart phone. Otherwise, continuing iteration, namely taking the position coordinates of the middle intelligent mobile phone and the middle Zhong Chazhi as new initial intelligent mobile phone position coordinates and new initial Zhong Chazhi, jumping to execute the steps of updating the initial intelligent mobile phone position coordinates and the initial clock difference values corresponding to the positioning equation by adopting a preset updating formula, generating the difference values of the middle intelligent mobile phone position coordinates and the middle clock, and counting the iteration times. And when the iteration times exceed the preset iteration times, the position calculation fails.

And if the updated solution meets the precision requirement, solving to obtain the high-precision position information of the intelligent mobile phone. If the solution does not meet the precision requirement, S _k,δt_u,k may be used as a starting point of the (k+1) th iteration to continue the newton iteration operation. And acquiring GNSS original data pseudo-range observed quantity under a tight combination fusion strategy to perform pseudo-range positioning, coupling altimeter height data into a positioning equation to perform least square solution, and finally obtaining a three-dimensional positioning result.

And 206, when the scene type is an open environment scene, performing data fusion positioning based on the carrier-to-noise ratio average value, the pseudo-range multipath error average value, the barometric altimeter data and the global navigation satellite system data, and generating second positioning data corresponding to the smart phone.

Further, step 206 may include the following substeps S41-S42:

S41, when the average value of the pseudo-range multipath errors is smaller than a third preset value and the average value of the carrier-to-noise ratio is larger than or equal to a fourth preset value, performing data fusion positioning by adopting the barometric altimeter data and the global navigation satellite system data according to a loose combination fusion strategy, and generating first three-dimensional positioning data corresponding to the smart phone.

S42, when the pseudo-range multipath error average value is in a first preset interval and the carrier-to-noise ratio average value is in a second preset interval, carrying out data fusion positioning by adopting the barometric altimeter data and the global navigation satellite system data according to a tightly combined fusion strategy, and generating second three-dimensional positioning data corresponding to the smart phone.

Further, step S41 may comprise the following sub-steps S411-S414:

S411, inputting the barometric altimeter data and the global navigation satellite system data into a preset Kalman filtering model to perform state variable estimation, and generating an estimated state vector.

S412, substituting the gain matrix, the measurement matrix and the estimated state vector corresponding to the estimated state vector into a preset state variable estimation formula, and calculating to obtain the Kalman filtering state quantity estimated value.

S413, subtracting the initial altitude data in the Kalman filtering state quantity estimation value from the altitude data in the global navigation satellite system data to generate target altitude data.

And S414, updating coordinate data corresponding to the Kalman filtering state quantity estimation value by adopting the target height data to generate first three-dimensional positioning data corresponding to the smart phone.

The third preset value is 3m. The fourth preset value is 35dB-Hz. The first preset interval is 3m or less, and the average value of the pseudo-range multipath errors is 5m or less. The second preset interval is 35dB-Hz, the average value of the carrier-to-noise ratio is more than or equal to 30dB-Hz.

In the embodiment of the invention, when the average value of the pseudo-range multipath errors is smaller than 3m and the average value of the carrier-to-noise ratio is larger than or equal to 35dB-Hz, a loose combination fusion strategy is adopted to conduct data fusion positioning to generate second positioning data corresponding to the smart phone, otherwise, when the average value of the pseudo-range multipath errors is smaller than or equal to 3m and is smaller than or equal to 5m and the average value of the carrier-to-noise ratio is larger than or equal to 35dB-Hz, data fusion positioning is conducted according to the tight combination fusion strategy to generate second positioning data corresponding to the smart phone. The central idea of the loose combination fusion strategy is to utilize the calculated height data of the barometric pressure altitude to directly carry out Kalman filtering with the height data in the GNSS three-dimensional position, so as to realize the fusion of the multi-source data.

The following describes a loose combination fusion strategy, i.e. a filter estimation algorithm flow for altitude data fusion of GNSS, barometric altimeters, wherein the rate of change of GNSS positioning data, altitude data is a component in state variables, and other matrices are other variations of these state variables. The GNSS sensor outputs three-dimensional position coordinate data (x, y, z _g), and the barometric altimeter PS outputs altitude data z _p.

Based on the Kalman filter model, the state equation and the measurement equation can be expressed as:

Wherein X _f is a state variable matrix of time f, X _f＝[Δx,Δy,Δz];Φ_f,f-1 is a one-step transfer matrix from time f-1 to time f, Z _f is a measurement matrix, Z _f＝z_g-z_p;H_f is a measurement matrix, its expression is [0, 1] ^T;W_f-1 is a system noise matrix of time f-1, and V _f is a measurement noise matrix of time f.

Let W _f,V_f be a zero-mean Gaussian white noise that is generally uncorrelated, the following condition is satisfied:

Wherein W _f represents a time f system noise matrix, V _f represents a time f measurement noise matrix, Q _f represents a system noise covariance matrix, W _j ^T represents a transpose of a time j system noise matrix, V _j ^T represents a transpose of a time j measurement noise matrix, and R _f represents a covariance matrix of measurement noise; Is a Kronecker (Kronecker) function. If the system noise covariance matrix Q _f is a non-negative fixed matrix and the measured noise covariance matrix R _f is a positive fixed matrix, the one-step state prediction equation is:

Wherein, In order to estimate a state vector from time f-1 to time f, the state vector is a state variable X _f estimated by a Kalman filter, and the state variable is a three-dimensional position change quantity corresponding to mobile phone positioning data, and X _f = [ delta X, delta y, delta z ]; Refers to the estimated state vector at time k-1, and phi _f,f-1 is the one-step transition matrix from time f-1 to time f.

The one-step estimation error covariance matrix equation is:

Wherein, P _f,f-1 is the one-step estimation error covariance matrix from time f to time f-1, P _f-1 is the estimation error covariance matrix at time f-1; The method is characterized in that the method is a transpose of a one-step transfer matrix phi _f,f-1 from time f to time f-1, and Q _f-1 is a system noise variance matrix at time f-1; The transpose of matrix Γ _f-1 is driven for system noise.

The gain matrix expression is:

Wherein K _f is the gain matrix; Transpose of the measurement matrix H _f, P _f,f-1 is the one-step estimation error covariance matrix from time f to time f-1, and R _f is the covariance matrix of the measurement noise.

Covariance matrix estimation:

Wherein P _f is a one-step estimation error covariance matrix of time f, H _f is a measurement matrix, the expression of which is set as [0, 1] ^T;P_f,f-1 is a one-step estimation error covariance matrix from time f to time f-1, I=diag (1, 1.., 1) is a unit matrix, and R _f is a covariance matrix of measurement noise; Is a transpose of the gain matrix K _f.

The preset state variable estimation formula is:

Wherein, The state quantity estimation value is Kalman filtering state quantity estimation value; The estimated state vector from time f-1 to time f is a state variable X _f estimated by a Kalman filter, wherein the state variable is a three-dimensional position variable corresponding to mobile phone positioning data, X _f＝[Δx,Δy,Δz];K_f is set as a gain matrix, Z _f is set as a measurement matrix, Z _f＝z_g-z_p,z_g is set as altitude data in global navigation satellite system data, Z _p is altitude data in barometric altimeter data, H _f is set as a measurement matrix, and the expression of the state variable is set as [0,1] ^T.

And inputting the barometric altimeter data and the global navigation satellite system data into a preset Kalman filtering model to perform state variable estimation to obtain an estimated state vector. And substituting the gain matrix, the measurement matrix and the estimated state vector corresponding to the estimated state vector into a preset state variable estimation formula, and calculating to obtain the Kalman filtering state quantity estimated value. Then subtracting the Kalman filtering state quantity estimation value from the altitude data zg in the global navigation satellite system data in the GNSS three-dimensional positionAnd finally obtaining a fusion positioning result (x, y, z). Namely, the initial altitude data in the Kalman filtering state quantity estimation value is subtracted from the altitude data in the global navigation satellite system data to generate target altitude data. And updating coordinate data corresponding to the Kalman filtering state quantity estimation value by adopting the target height data to generate first three-dimensional positioning data corresponding to the smart phone.

In the embodiment of the invention, the data fusion algorithm of two sensors of a Global Navigation Satellite System (GNSS) and an barometer (PS) is combined to improve the accuracy and the robustness of acquiring global position information by the smart phone. The data fusion strategy can be expanded from a satellite navigation and air pressure altimeter to a multi-sensor fusion mode such as satellite navigation and inertial navigation and air pressure altimeter, satellite navigation and inertial navigation and visual navigation and air pressure altimeter and the like. And adding related variables of an Inertial Navigation System (INS), such as position errors, speed errors and the like, into the state variables, and then estimating the errors by Kalman filtering to obtain accurate positions, thereby realizing a data loose combination strategy of satellite navigation, inertial navigation and barometric altimeter. Position errors, speed errors and the like of the visual navigation system are added into the state variables, so that a satellite navigation, inertial navigation, visual navigation and air pressure altitude count loose combination strategy is realized.

Referring to fig. 3, fig. 3 is a block diagram illustrating a positioning system of a smart phone according to a third embodiment of the present invention.

The third embodiment of the invention provides a smart phone positioning system, which comprises:

The average value generating module 301 is configured to obtain global navigation satellite system data and barometric altitude data collected by the smart phone, and calculate a carrier-to-noise ratio average value and a pseudo-range multipath error average value based on the global navigation satellite system data, respectively, to generate the carrier-to-noise ratio average value and the pseudo-range multipath error average value.

The scene type determining module 302 is configured to perform environmental scene recognition according to the average value of the carrier-to-noise ratio and the average value of the pseudo-range multipath error, and determine the scene type.

The first positioning data generating module 303 is configured to perform data fusion positioning by using the barometric altimeter data and the global navigation satellite system data according to a tightly combined fusion policy when the scene type is a multipath severe scene, and generate first positioning data corresponding to the smart phone.

And the second positioning data generating module 304 is configured to perform data fusion positioning based on the carrier-to-noise ratio average value, the pseudo-range multipath error average value, the barometric altimeter data and the global navigation satellite system data when the scene type is an open environment scene, and generate second positioning data corresponding to the smart phone.

Optionally, the global navigation satellite system data includes a plurality of satellite signal wavelengths, a plurality of ionospheric errors, a plurality of satellite signal powers, a plurality of bilateral noise power spectral densities, a plurality of pseudorange values, a plurality of carrier phase values, and a plurality of carrier phase ambiguities. The average value generation module 301 may perform the following steps:

calculating the sum value among all the carrier-to-noise ratios, and generating a carrier-to-noise ratio sum value;

calculating the ratio between the sum of the carrier-to-noise ratios and the corresponding carrier-to-noise ratio number to generate a carrier-to-noise ratio average value;

the preset pseudo-range multipath error expression is:

mp=ρ-Φ·λ-2I+N_iλ;

Calculating the sum value among all pseudo-range multipath errors to generate a pseudo-range multipath error sum value;

and calculating the ratio between the sum value of the pseudo-range multipath errors and the corresponding error quantity, and generating a pseudo-range multipath error average value.

Alternatively, the scene type determination module 302 may perform the following steps:

When the average value of the carrier-to-noise ratio is smaller than a first preset value and the average value of the pseudo-range multipath errors is larger than a second preset value, the scene type is a multipath serious scene;

when the average value of the carrier-to-noise ratio is larger than or equal to a first preset value and the average value of the pseudo-range multipath errors is smaller than a second preset value, the scene type is an open environment scene.

Optionally, the first positioning data generating module 303 includes:

the quaternary nonlinear equation construction module is used for constructing a quaternary nonlinear equation by adopting the satellite three-dimensional position, the smart phone three-dimensional position, the pseudo-range observation correction value and the smart phone clock difference in the global navigation satellite system data when the scene type is a multipath severe scene.

And the linearization positioning matrix equation generation module is used for linearizing the quaternary nonlinear equation to generate a linearization positioning matrix equation.

And the first positioning data generation sub-module is used for carrying out iterative solution on the linearization positioning matrix equation by adopting a Newton iteration method and determining the first positioning data corresponding to the smart phone.

Alternatively, the first positioning data generation sub-module may perform the steps of:

Updating the initial smart phone position coordinates and the initial clock difference value corresponding to the positioning equation by adopting a preset updating formula, generating intermediate smart phone position coordinates and intermediate clock difference values, and counting the iteration times;

The preset updating formula is as follows:

δt_u,k＝δt_u,k-1+Δδt_u;

Substituting the position coordinates of the middle intelligent mobile phone and the middle clock difference value into a preset precision calculation formula to generate a precision value when the iteration times are smaller than or equal to the preset iteration times;

The preset precision calculation formula is as follows:

when the precision value is smaller than a preset threshold value, taking the middle intelligent mobile phone position coordinate and the middle Zhong Chazhi as the target intelligent mobile phone position coordinate and the target Zhong Chazhi;

coupling the altitude variation in the barometric altimeter data, the target smart phone position coordinate and the target Zhong Chazhi into a preset positioning equation to carry out least square solution to obtain first positioning data corresponding to the smart phone;

The preset positioning equation is:

When the precision value is larger than a preset threshold value, taking the position coordinate of the middle intelligent mobile phone and the middle Zhong Chazhi as a new initial intelligent mobile phone position coordinate and a new initial Zhong Chazhi, skipping to execute the steps of updating the initial intelligent mobile phone position coordinate and the initial clock difference value corresponding to the positioning equation by adopting a preset updating formula, generating the position coordinate of the middle intelligent mobile phone and the middle clock difference value, and counting the iteration times.

Optionally, the second positioning data generating module 304 includes:

And the first three-dimensional positioning data generation module is used for carrying out data fusion positioning by adopting the barometric altimeter data and the global navigation satellite system data according to a loose combination fusion strategy when the average value of the pseudo-range multipath errors is smaller than a third preset value and the average value of the carrier-to-noise ratio is larger than or equal to a fourth preset value, so as to generate first three-dimensional positioning data corresponding to the smart phone.

And the second three-dimensional positioning data generation module is used for carrying out data fusion positioning by adopting the barometric altimeter data and the global navigation satellite system data according to a tightly combined fusion strategy when the pseudo-range multipath error average value is in a first preset interval and the carrier-to-noise ratio average value is in a second preset interval, so as to generate second three-dimensional positioning data corresponding to the smart phone.

Alternatively, the first three-dimensional positioning data generation module may perform the steps of:

Inputting barometric altimeter data and global navigation satellite system data into a preset Kalman filtering model to perform state variable estimation, and generating an estimated state vector;

Substituting a gain matrix, a measurement matrix and an estimated state vector corresponding to the estimated state vector into a preset state variable estimation formula, and calculating to obtain a Kalman filtering state quantity estimated value;

the preset state variable estimation formula is:

Referring to fig. 4, fig. 4 is a block diagram of an electronic device according to a third embodiment of the present invention.

The electronic device of the embodiment of the invention comprises a memory 401 and a processor 402, wherein the memory 401 stores a computer program, and the computer program when executed by the processor 402 causes the processor 402 to execute the smart phone positioning method according to any of the embodiments.

The memory 401 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. Memory 401 has storage space 403 for program code 413 for performing any of the method steps described above. For example, the memory space 403 for program code may include individual program code 413 for implementing the various steps in the above method, respectively. The program code can be read from or written to one or more computer program products. These computer program products comprise a program code carrier such as a hard disk, a Compact Disc (CD), a memory card or a floppy disk. The program code may be compressed, for example, in a suitable form. The codes, when executed by a computing processing device, cause the computing processing device to perform the steps in the smartphone positioning method described above.

The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the smart phone positioning method according to any of the above embodiments.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present invention. The storage medium includes a U disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, an optical disk, or other various media capable of storing program codes.

The foregoing embodiments are merely for illustrating the technical solution of the present invention, but not for limiting the same, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that modifications may be made to the technical solution described in the foregoing embodiments or equivalents may be substituted for parts of the technical features thereof, and that such modifications or substitutions do not depart from the spirit and scope of the technical solution of the embodiments of the present invention in essence.

Claims

1. The smart phone positioning method is characterized by comprising the following steps of:

2. The method of claim 1, wherein the global navigation satellite system data includes a plurality of satellite signal wavelengths, a plurality of ionospheric errors, a plurality of satellite signal powers, a plurality of bilateral noise power spectral densities, a plurality of pseudorange values, a plurality of carrier phase values, and a plurality of carrier phase ambiguities, and wherein the steps of calculating a carrier-to-noise ratio average and a pseudorange multipath error average based on the global navigation satellite system data, respectively, and generating a carrier-to-noise ratio average and a pseudorange multipath error average comprise:

The preset pseudo-range multipath error expression is:

mp=ρ-Φ·λ-2I+N_iλ;

3. The smart phone positioning method according to claim 1, wherein the step of identifying an environmental scene and determining a scene type according to the carrier-to-noise ratio average value and the pseudo-range multipath error average value includes:

4. The method for positioning a smart phone according to claim 1, wherein when the scene type is a multipath severe scene, the step of performing data fusion positioning by using the barometric altimeter data and the global navigation satellite system data according to a tightly combined fusion policy to generate first positioning data corresponding to the smart phone comprises the following steps:

5. The method for positioning a smart phone according to claim 4, wherein the step of iteratively solving the linearized positioning matrix equation by using newton's iteration method to determine the first positioning data corresponding to the smart phone comprises:

the preset updating formula is as follows:

δt_u,k＝δt_u,k-1+Δδt_u;

The preset precision calculation formula is as follows:

The preset positioning equation is as follows:

6. The method for positioning a smart phone according to claim 1, wherein the step of generating second positioning data corresponding to the smart phone based on the carrier-to-noise ratio average value, the pseudo-range multipath error average value, the barometric altimeter data and the global navigation satellite system data by performing data fusion positioning includes:

7. The method for positioning a smart phone according to claim 6, wherein the step of performing data fusion positioning by using the barometric altimeter data and the global navigation satellite system data according to a loose combination fusion policy to generate first three-dimensional positioning data corresponding to the smart phone comprises the following steps:

the preset state variable estimation formula is as follows:

8. A smartphone positioning system, comprising:

9. An electronic device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the smartphone positioning method of any one of claims 1 to 7.

10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed, implements the smartphone positioning method according to any one of claims 1 to 7.