CN119334341A - Error compensation method, device and readable storage medium for navigation system - Google Patents
Error compensation method, device and readable storage medium for navigation system Download PDFInfo
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Abstract
本发明公开了一种导航系统的误差补偿方法、装置和可读存储介质。其中，该方法包括：获取导航系统的基础数据，其中，基础数据至少包括导航系统中移动设备的定位数据和/或运动状态数据，定位数据用于指示移动设备的位置，运动状态数据用于指示移动设备的速度和/或方向；基于基础数据，确定导航系统的误差补偿策略，其中，误差补偿策略用于指示对导航系统进行补偿的规则；基于基础数据与误差补偿策略，确定导航系统的误差补偿参数；按照误差补偿参数，对导航系统进行补偿，其中，补偿后的导航系统相对于补偿前的导航系统的误差小于目标阈值。本发明解决了导航系统无法稳定运行的技术问题。
The present invention discloses an error compensation method, device and readable storage medium for a navigation system. The method comprises: obtaining basic data of the navigation system, wherein the basic data at least includes positioning data and/or motion state data of a mobile device in the navigation system, the positioning data is used to indicate the position of the mobile device, and the motion state data is used to indicate the speed and/or direction of the mobile device; based on the basic data, determining the error compensation strategy of the navigation system, wherein the error compensation strategy is used to indicate the rules for compensating the navigation system; based on the basic data and the error compensation strategy, determining the error compensation parameters of the navigation system; according to the error compensation parameters, compensating the navigation system, wherein the error of the compensated navigation system relative to the error of the navigation system before compensation is less than the target threshold. The present invention solves the technical problem that the navigation system cannot operate stably.
Description
Technical Field
The present invention relates to the technical field of navigation calibration, and in particular, to a method and apparatus for error compensation of a navigation system, and a readable storage medium.
Background
Currently, in conventional integrated navigation systems, the combined use of a global navigation satellite system (Global Navigation SATELLITE SYSTEM, abbreviated as GNSS) and an inertial navigation system (Inertial Navigation System, abbreviated as INS) provides high-precision position and attitude estimation for various navigation scenarios. However, GNSS signals are susceptible to interference from factors such as occlusion, reflection, etc. in complex environments, e.g., urban canyons, forest areas or underground spaces, even when the signals are completely interrupted. This signal instability results in the system not continuously providing reliable high accuracy positioning information.
In the related art, although the INS can maintain a positioning function by means of an inertial sensor in a short time, navigation accuracy is rapidly lowered due to problems of drift, noise and accumulated errors inherent to the sensor, so that it is difficult to satisfy practical demands for long-term independent use of the INS. Therefore, there is a technical problem in that the navigation system cannot be stably operated.
Aiming at the technical problem that the navigation system cannot stably operate, no effective solution is proposed at present.
Disclosure of Invention
The embodiment of the invention provides an error compensation method and device of a navigation system and a readable storage medium, which are used for at least solving the technical problem that the navigation system cannot stably run.
According to an aspect of an embodiment of the present invention, there is provided an error compensation method of a navigation system. The method comprises the steps of obtaining basic data of a navigation system, wherein the basic data at least comprise positioning data and/or motion state data of mobile equipment in the navigation system, the positioning data are used for indicating the position of the mobile equipment, the motion state data are used for indicating the speed and/or direction of the mobile equipment, determining an error compensation strategy of the navigation system based on the basic data, wherein the error compensation strategy is used for indicating a rule for compensating the navigation system, determining error compensation parameters of the navigation system based on the basic data and the error compensation strategy, and compensating the navigation system according to the error compensation parameters, wherein the error of the navigation system after compensation relative to the navigation system before compensation is smaller than a target threshold value.
Optionally, determining the error compensation strategy of the navigation system based on the basic data comprises determining that the error compensation strategy of the navigation system is a first compensation strategy in response to the positioning data being valid, and determining that the error compensation strategy of the navigation system is a second compensation strategy in response to the positioning data being invalid, wherein the first compensation strategy is different from the second compensation strategy in the way the error compensation parameters are determined.
Optionally, determining the error compensation parameter of the navigation system based on the basic data and the error compensation strategy includes inputting the motion state data into a first compensation model to obtain position increment data in response to the error compensation strategy being a first compensation strategy, wherein the first compensation model is obtained by training with historical motion state data, and fusing the position increment data with positioning data to obtain the error compensation parameter.
Optionally, determining the error compensation parameter of the navigation system based on the basic data and the error compensation strategy includes inputting the positioning data into a second compensation model to obtain pseudo-position data in response to the error compensation strategy being a second compensation strategy, wherein the second compensation model is obtained by training with historical positioning data, and fusing the pseudo-position data with motion state data to obtain the error compensation parameter.
Optionally, compensating the navigation system according to the error compensation parameter includes inputting the error compensation parameter into the navigation system for compensation.
Optionally, the error compensation method of the navigation system further comprises calibrating inertial sensors in the navigation system before acquiring the base data of the navigation system.
According to another aspect of the embodiment of the invention, an error compensation device of a navigation system is also provided. The device comprises an acquisition unit, a first determination unit and a second determination unit, wherein the acquisition unit is used for acquiring basic data of a navigation system, the basic data at least comprise positioning data and/or motion state data, the positioning data are used for indicating the position of a mobile device in the navigation system, the motion state data are used for indicating the speed and/or direction of the mobile device in the navigation system, the first determination unit is used for determining an error compensation strategy of the navigation system based on the basic data, the error compensation strategy is used for indicating a rule for compensating the navigation system, the second determination unit is used for determining error compensation parameters of the navigation system based on the basic data and the error compensation strategy, and the compensation unit is used for compensating the error of the navigation system according to the error compensation parameters, wherein the error of the navigation system after compensation relative to the navigation system before compensation is smaller than a target threshold value.
According to another aspect of the embodiment of the present invention, there is also provided a computer-readable storage medium including a stored program, where the program when executed by a processor controls a device in which the storage medium is located to perform the error compensation method of the navigation system in the embodiment of the present invention.
According to another aspect of an embodiment of the present invention, there is also provided a processor. The processor is used for running a program, wherein the error compensation method of the navigation system in the embodiment of the invention is executed when the program runs.
According to another aspect of an embodiment of the present invention, a computer program product is also provided. The program product comprises computer instructions which, when executed by a processor, implement the error compensation method of the navigation system in an embodiment of the invention.
In the embodiment of the invention, basic data of a navigation system is acquired, wherein the basic data at least comprises positioning data and/or motion state data of a mobile device in the navigation system, the positioning data is used for indicating the position of the mobile device, the motion state data is used for indicating the speed and/or direction of the mobile device, an error compensation strategy of the navigation system is determined based on the basic data, the error compensation strategy is used for indicating a rule for compensating the navigation system, error compensation parameters of the navigation system are determined based on the basic data and the error compensation strategy, and the navigation system is compensated according to the error compensation parameters, wherein the error of the navigation system after compensation relative to the navigation system before compensation is smaller than a target threshold value. That is, the invention determines the error compensation strategy and the error compensation parameter according to the basic data of the navigation system, thereby performing error compensation on the navigation system, further solving the technical problem that the navigation system cannot stably operate, and realizing the technical effect of improving the stability of the navigation system.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flow chart of a method of error compensation for a navigation system in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a GNSS/INS integrated navigation error compensation method according to an embodiment of the invention;
FIG. 3 is a schematic diagram of a GNSS/INS loosely coupled architecture and Kalman filtering process in accordance with an embodiment of the invention;
FIG. 4 is a schematic diagram of a GNSS/INS integrated navigation system model training phase in accordance with an embodiment of the present invention;
FIG. 5 is a schematic diagram of a model prediction phase of a GNSS/INS integrated navigation system in accordance with an embodiment of the invention;
fig. 6 is a schematic diagram of an error compensation apparatus of a navigation system according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, functional unit, or apparatus that comprises a list of steps or units is not necessarily limited to those steps or units that are expressly listed or inherent to such process, method, functional unit, or apparatus.
According to an embodiment of the present invention, there is provided an embodiment of an error compensation method of a navigation system, it being noted that the steps shown in the flowcharts of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that herein.
FIG. 1 is a flow chart of a method of error compensation for a navigation system according to an embodiment of the invention, as shown in FIG. 1, the method may include the steps of:
Step S101, obtaining basic data of a navigation system.
In the technical solution provided in the above step S101 of the present invention, the basic data at least includes positioning data and/or motion state data of the mobile device in the navigation system, where the positioning data is used to indicate the position of the mobile device, and the motion state data is used to indicate the speed and/or direction of the mobile device. The positioning data of the mobile device may be GNSS data, and the motion state data may be INS data.
In this embodiment, the basic data of the navigation system is acquired, for example, using a GNSS receiver and an INS sensor, which are only exemplary examples herein, and a specific method of acquiring the basic data of the navigation system is not limited.
Alternatively, by acquiring positioning data and motion state data of the mobile device in the navigation system, positioning and navigation accuracy and reliability can be improved.
Step S102, determining an error compensation strategy of the navigation system based on the basic data.
In the technical solution provided in the above step S102 of the present invention, the error compensation policy is used to indicate a rule for compensating the navigation system.
In this embodiment, after the basic data of the navigation system is acquired in step S101, an error compensation policy of the navigation system is determined according to the basic data.
Optionally, when the positioning data is valid, it indicates that the received satellite signal is strong and stable enough to provide accurate position information, based on which an error compensation policy of the navigation system can be determined as a first compensation policy, and when the positioning data is invalid, it indicates that the received satellite signal is unstable, based on which an error compensation policy of the navigation system can be determined as a second compensation policy, wherein the first and second compensation policies differ in the manner in which the error compensation parameters are determined.
Step S103, determining error compensation parameters of the navigation system based on the basic data and the error compensation strategy.
In the technical scheme provided in the step S103, after determining the error compensation strategy of the navigation system in the step S102, determining the error compensation parameter of the navigation system according to the basic data and the error compensation strategy.
In this embodiment, when the error compensation strategy is the first compensation strategy, the motion state data is input into the first compensation model to obtain the position increment data, where the first compensation model is obtained by training using the historical motion state data, so that the position increment data and the positioning data are fused to obtain the error compensation parameter.
For example, when GNSS data can be used, a first compensation model is trained using historical INS data, where the input and output of the first compensation model can be expressed as the following equation (1):
Input:[θγψfX fY fZωXωYωZ]
and outputting the position increment of the GNSS according to the first compensation model, and then combining the original data of the GNSS to perform filtering fusion to obtain error compensation parameters. The position increment can be expressed by a quadratic integral form of a specific force equation, as shown in the following formula (2):
Optionally, when the error compensation strategy is the second compensation strategy, the positioning data is input into a second compensation model to obtain pseudo-position data, wherein the second compensation model is obtained by training with historical positioning data, and therefore the pseudo-position data and the motion state data are fused to obtain the error compensation parameter.
For example, when the GNSS signal is not available, the second compensation model is trained by using the historical positioning data, and the pseudo GNSS data is output according to the second compensation model, wherein the pseudo GNSS data is calculated as shown in the following formula (3):
Wherein, Is the pseudo-GNSS data at time k,The position of the carrier before interruption of the GNSS signals is interrupted, so that the fusion of the pseudo GNSS data and the INS data is interfered, and error compensation parameters are obtained.
Step S104, compensating the navigation system according to the error compensation parameters.
In the technical solution provided in the above step S104 of the present invention, the error of the compensated navigation system with respect to the navigation system before compensation is smaller than the target threshold.
In this embodiment, after the error compensation parameter is determined in step S103, the navigation system is compensated according to the error compensation parameter.
Optionally, the error compensation parameter is input into the navigation system for compensation.
It should be noted that the above embodiments may be performed by an error compensation device of a navigation system.
The method comprises the steps of S101 to S104, obtaining basic data of a navigation system, wherein the basic data at least comprise positioning data and/or motion state data of a mobile device in the navigation system, the positioning data are used for indicating the position of the mobile device, the motion state data are used for indicating the speed and/or direction of the mobile device, determining an error compensation strategy of the navigation system based on the basic data, wherein the error compensation strategy is used for indicating a rule for compensating the navigation system, determining error compensation parameters of the navigation system based on the basic data and the error compensation strategy, and compensating the navigation system according to the error compensation parameters, wherein the error of the navigation system after compensation relative to the navigation system before compensation is smaller than a target threshold value. That is, the invention determines the error compensation strategy and the error compensation parameter according to the basic data of the navigation system, thereby performing error compensation on the navigation system, further solving the technical problem that the navigation system cannot stably operate, and realizing the technical effect of improving the stability of the navigation system.
The above-described method of this embodiment is further described below.
As an alternative embodiment, determining the error compensation strategy of the navigation system based on the base data comprises determining that the error compensation strategy of the navigation system is a first compensation strategy in response to the positioning data being valid, and determining that the error compensation strategy of the navigation system is a second compensation strategy in response to the positioning data being invalid, wherein the first compensation strategy is different from the second compensation strategy in the way the error compensation parameters are determined.
In this embodiment, the error compensation strategy of the navigation system is determined to be a first compensation strategy when the positioning data is valid, and is determined to be a second compensation strategy when the positioning data is invalid.
Optionally, by determining different error compensation strategies, the error of the navigation system is comprehensively compensated, so that the stability of the navigation system is improved.
As an alternative embodiment, determining the error compensation parameter of the navigation system based on the basic data and the error compensation strategy comprises inputting the motion state data into a first compensation model to obtain position increment data in response to the error compensation strategy being a first compensation strategy, wherein the first compensation model is obtained by training by using historical motion state data, and fusing the position increment data with the positioning data to obtain the error compensation parameter.
In this embodiment, when the error compensation strategy is the first compensation strategy, the motion state data is input into the first compensation model to obtain position increment data, and the position increment data and the positioning data are fused to obtain the error compensation parameter.
For example, in training the first compensation model, 15 state vectors are used in both the first compensation model construction and experimental analysis, i.e., the input of the model can be expressed as the following equation (4):
and fusing the position increment data with the positioning data to obtain error compensation parameters.
For another example, the first compensation model improves the accuracy of the navigation system by compensating for errors between specific force, angular rate, inertial navigation attitude and GNSS position delta when GNSS data is available.
As an alternative embodiment, determining the error compensation parameter of the navigation system based on the basic data and the error compensation strategy comprises inputting the positioning data into a second compensation model to obtain pseudo-position data in response to the error compensation strategy being a second compensation strategy, wherein the second compensation model is obtained by training with historical positioning data, and fusing the pseudo-position data with motion state data to obtain the error compensation parameter.
In this embodiment, when the error compensation strategy is the second compensation strategy, the positioning data is input into the second compensation model to obtain pseudo-position data, and the pseudo-position data and the motion state data are fused to obtain the error compensation parameter.
For example, when GNSS data is not available, the integrated navigation system operates under a separate INS system, and the positioning error is continuously diverged with time. At present, the navigation system is corrected by using the trained model to output pseudo GNSS signals.
As an alternative embodiment, compensating the navigation system according to the error compensation parameter includes inputting the error compensation parameter into the navigation system for compensation.
In this embodiment, the error compensation parameter is input into the navigation system to compensate, and the accuracy of the navigation system is improved.
As an alternative embodiment, the method further comprises calibrating inertial sensors in the navigation system prior to acquiring the base data of the navigation system.
In this embodiment, there is zero bias and scale factor error for the gyroscopes and accelerometers due to manufacturing process and installation errors, requiring calibration of inertial sensors in the navigation system.
For example, inertial sensors typically include gyroscopes and accelerometers that have zero bias and scale factor errors due to manufacturing process and installation errors, which are root causes of positioning error divergence, requiring calibration and compensation prior to solution. The positional error equation is expressed as the following equation (5):
Wherein δL, δλ and δh are latitude, longitude and altitude errors, δv E,δvN and δv U are east, north and upward velocity errors, respectively, and R M and R N represent radii of curvature on the meridian and the primary vertical.
It should be noted that the above embodiments may be performed by an error compensation device of a navigation system.
In this embodiment, basic data of the navigation system is obtained, wherein the basic data at least comprises positioning data and/or motion state data of a mobile device in the navigation system, the positioning data is used for indicating the position of the mobile device, the motion state data is used for indicating the speed and/or direction of the mobile device, an error compensation strategy of the navigation system is determined based on the basic data, wherein the error compensation strategy is used for indicating a rule for compensating the navigation system, error compensation parameters of the navigation system are determined based on the basic data and the error compensation strategy, and the navigation system is compensated according to the error compensation parameters, wherein an error of the navigation system after compensation relative to the navigation system before compensation is smaller than a target threshold. That is, the invention determines the error compensation strategy and the error compensation parameter according to the basic data of the navigation system, thereby performing error compensation on the navigation system, further solving the technical problem that the navigation system cannot stably operate, and realizing the technical effect of improving the stability of the navigation system.
The technical solution of the embodiment of the present invention will be illustrated in the following with reference to a preferred embodiment.
Currently, in conventional integrated navigation systems, the combined use of a global navigation satellite system (Global Navigation SATELLITE SYSTEM, abbreviated as GNSS) and an inertial navigation system (Inertial Navigation System, abbreviated as INS) provides high-precision position and attitude estimation for various navigation scenarios. However, GNSS signals are susceptible to interference from factors such as occlusion, reflection, etc. in complex environments, e.g., urban canyons, forest areas or underground spaces, even when the signals are completely interrupted. This signal instability results in the system not continuously providing reliable high accuracy positioning information.
In the related art, although the INS can maintain a positioning function by means of an inertial sensor in a short time, navigation accuracy is rapidly lowered due to problems of drift, noise and accumulated errors inherent to the sensor, so that it is difficult to satisfy practical demands for long-term independent use of the INS. Therefore, there is a technical problem in that the navigation system cannot be stably operated. Aiming at the technical problem that the navigation system cannot stably operate, no effective solution is proposed at present.
However, the embodiment of the invention provides an error compensation method of a GNSS/INS combined navigation system based on an AT-LSTM neural network, which is used for acquiring basic data of the navigation system, wherein the basic data can comprise GNSS data and INS data, training a model by using historical basic data, and inputting different parameters into different compensation models according to different conditions, namely, the condition that GNSS signals are available and the condition that the GNSS signals are unavailable, so as to obtain error compensation parameters, and compensating the error compensation parameters and the navigation system. The technical problem that the navigation system cannot stably run is solved, and the technical effect that the instability of the navigation system is improved is achieved.
Embodiments of the present invention are further described below.
FIG. 2 is a schematic diagram of a GNSS/INS integrated navigation error compensation method according to an embodiment of the invention, as shown in FIG. 2, an inertial sensor generally comprises a gyroscope and an accelerometer, which have zero bias and scale factor errors due to manufacturing process and installation errors, which are the root causes of the divergence of positioning errors. Therefore, calibration and compensation are required before solving.
In this embodiment, the position error equation may be expressed as the foregoing equation (5), and a detailed description thereof will be omitted.
Alternatively, the GNSS data is data for acquiring position, velocity, and time information by receiving satellite signals through a Global Navigation Satellite System (GNSS).
Optionally, the INS data is position, velocity and attitude information of the aircraft, vessel or vehicle obtained by an Inertial Navigation System (INS). The INS system measures the motion state of the aircraft through sensors such as an accelerometer, a gyroscope and a magnetometer, so that navigation and positioning are realized.
Alternatively, GNSS data and INS data are typically used in combination to provide more accurate and reliable navigation information. The INS system may provide stable position and attitude information, while the GNSS system may provide updated position information. By fusing these two data, a higher accuracy navigation solution can be obtained.
Alternatively, the inertial sensor is a sensor capable of measuring the state of motion of an object in space. It determines the motion trajectory and pose of an object by measuring the acceleration and angular velocity of the object. Common inertial sensors include accelerometers and gyroscopes, which are commonly used in the fields of navigation systems, aircraft, automobiles, and virtual reality. The inertial sensor works on the principle that the inertial characteristics of an object are used to measure the motion state of the object, so that the position and the posture of the object can be accurately determined in an environment without GPS signals.
FIG. 3 is a schematic diagram of a GNSS/INS loose coupling structure and a Kalman filtering process according to an embodiment of the invention, and as shown in FIG. 3, the GNSS/INS integrated navigation system can be divided into three types of loose coupling, tight coupling and ultra-tight coupling. The Kalman filtering is the most commonly used filtering algorithm in the GNSS/INS integrated navigation system, and the calculation process is a continuous prediction (time update) and correction (measurement update) process, so that the observation result can be conveniently processed in real time.
In this embodiment, the state equation and the measurement equation of the discrete kalman filter at the k time are expressed as the following equation (6):
Wherein X k denotes the state vector at time k, Φ k/k-1 denotes the state transition matrix, X k-1 denotes the state vector at time k-1, Γ k-1 denotes the noise matrix, W k-1 denotes the motion noise, obeying the Gaussian distribution N (0, qk-1), z k denotes the measurement value at time k, H k denotes the measurement matrix, and V k denotes the measurement noise.
Alternatively, the Kalman filtering is divided into two parts, time update and metric update. The calculation is a continuous prediction and correction process, and the equation of state prediction is shown as the following equation (7):
optionally, the state prediction variance is shown in the following formula (8):
Alternatively, the kalman gain is shown in the following formula (9):
optionally, the state estimate is as shown in the following equation (10):
alternatively, the error variance is shown in the following formula (11):
Pk＝(I-KkHk)Pk/k-1 (11)
Alternatively, the 15 state vector is used in the model construction and experimental analysis, and may be represented by the foregoing formula (4), and will not be described herein.
Alternatively, as can be seen from the INS update algorithm, the INS update algorithm is a process of continuously integrating and incrementing IMU (Inertial Measurement Unit) outputs. The relation model of INS information and GNSS position increment is established, the position increment can be expressed as the formula (2) in the form of quadratic integral of a specific force equation, the input and output of the model are expressed as the formula (1), and the description is omitted here.
Alternatively, the IMU is a device integrating a plurality of inertial sensors for measuring acceleration, angular velocity and direction of an object. Typical IMUs include three-axis accelerometers, three-axis gyroscopes, and three-axis magnetometers. These sensors may provide information about the position, direction and motion state of the object in space.
Alternatively, the IMU data is typically provided in a digital format, which may be transmitted to a microcontroller or computer for processing and analysis via a serial interface (e.g., SPI or I2C). Such data may be used for navigation, gesture control, motion detection, and other application areas.
Optionally, processing and analysis of IMU data requires consideration of techniques such as calibration between sensors, data fusion and filtering to ensure accurate and reliable results. IMUs are also often used in combination with other sensors (e.g., GPS, vision sensors) to improve the accuracy and stability of position and orientation estimation.
Alternatively, kalman filtering is an optimization algorithm for estimating the state of a system that combines information of predicted and observed values to optimize state estimation by minimizing the error between the predicted and observed values. The Kalman filtering is suitable for a dynamic system, can effectively process noise and uncertainty, and improves the accuracy and stability of state estimation. In practical applications, kalman filtering is commonly used in the fields of aerospace, navigation, robotics, and unmanned vehicles.
FIG. 4 is a schematic diagram of a training phase of a GNSS/INS integrated navigation system model according to an embodiment of the invention, and as shown in FIG. 4, the AT-LSTM based integrated navigation error compensation method is divided into two modes. When a GNSS is available, the model is trained.
In this embodiment, IMU data, i.e., specific force, angular velocity, and INS pose information, are used as inputs to the model during training. The position delta of the GNSS is used as the output of the model.
Alternatively, an AT-LSTM neural network (Attention-based Long Short-Term Memory) is a Long Short-Term Memory network (Long Short-Term Memory networks, abbreviated LSTM) based on an Attention mechanism for processing sequence data. In a conventional LSTM network, the model learns a representation of the entire input sequence, while AT-LSTM introduces a mechanism of attention, allowing the model to focus on different parts of the input sequence AT each time step.
Optionally, the AT-LSTM calculates an attention weight vector AT each time step for weighting different parts of the input sequence to adjust the attention of the model to the different parts. Therefore, the model can better capture important information in the input sequence, and the performance and generalization capability of the model are improved.
Optionally, the AT-LSTM neural network has good effects in the fields of natural language processing, voice recognition, time sequence prediction and the like, and becomes one of important models in deep learning. By introducing an attention mechanism, the AT-LSTM is better able to handle long sequence data and achieve better performance when handling complex tasks.
FIG. 5 is a schematic diagram illustrating a model prediction phase of a GNSS/INS integrated navigation system according to an embodiment of the invention, as shown in FIG. 5, when the GNSS signals are not available, the integrated navigation system operates under an independent INS system, and the positioning error is continuously diverged with time. At present, the trained model is used for outputting pseudo GNSS signals to correct the INS system.
In this embodiment, starting from the first epoch after GNSS is unavailable, the position increment (+ PGNSS) of the model output is accumulated to obtain the pseudo GNSS position of each epoch, as shown in the foregoing formula (3), which is not described herein. And integrating the pseudo GNSS position into the Kalman filtering, namely, performing individual fusion on the GNSS information and the IMU information, and outputting accurate information to realize error compensation of the integrated navigation system.
Optionally, the invention provides an error compensation method based on a long-short-term memory (LSTM) neural network integrated attention mechanism (AT-LSTM), which is used for a GNSS/INS integrated navigation system. When the GNSS signals are available, the model improves the accuracy of the navigation system by compensating for errors between specific force, angular rate, inertial navigation attitude and GNSS position increment. When the GNSS signals are interrupted, the error compensation model can output pseudo GNSS signals, compensate the integrated navigation system and inhibit divergence of positioning errors. The method obviously improves the positioning precision of the unmanned aerial vehicle under the condition of GNSS signal interruption.
In this embodiment, basic data of the navigation system is acquired, wherein the basic data may include GNSS data and INS data, the model is trained using historical basic data, different parameters are input into different compensation models according to different situations, that is, the GNSS signal is available and the GNSS signal is unavailable, error compensation parameters are obtained, and compensation is performed according to the error compensation parameters and the navigation system. The technical problem that the navigation system cannot stably run is solved, and the technical effect that the instability of the navigation system is improved is achieved.
According to the embodiment of the invention, an error compensation device of the navigation system is also provided. It should be noted that the error compensation device of the navigation system may be used to execute the error compensation method of the navigation system in the method embodiment.
Fig. 6 is a schematic diagram of an error compensation apparatus of a navigation system according to an embodiment of the present invention. As shown in fig. 6, the error compensation apparatus 600 of the navigation system may include an acquisition unit 601, a first determination unit 602, a second determination unit 603, and a compensation unit 604.
The obtaining unit 601 is configured to obtain basic data of the navigation system, where the basic data at least includes positioning data and/or motion state data, the positioning data is used to indicate a position of a mobile device in the navigation system, and the motion state data is used to indicate a speed and/or a direction of the mobile device in the navigation system.
A first determining unit 602, configured to determine an error compensation policy of the navigation system based on the basic data, where the error compensation policy is used to indicate a rule for compensating the navigation system.
The second determining unit 603 is configured to determine an error compensation parameter of the navigation system based on the base data and the error compensation policy.
And the compensation unit 604 is configured to compensate an error of the navigation system according to the error compensation parameter, where the error of the navigation system after compensation relative to the navigation system before compensation is less than the target threshold.
Optionally, the first determining unit 602 may include a first determining module configured to determine, in response to the positioning data being valid, that the error compensation policy of the navigation system is a first compensation policy, and a second determining module configured to determine, in response to the positioning data being invalid, that the error compensation policy of the navigation system is a second compensation policy, where the first compensation policy is different from the second compensation policy in a manner of determining the error compensation parameter.
Optionally, the second determining unit 603 may include a first input module, configured to input the motion state data into a first compensation model in response to the error compensation policy being the first compensation policy to obtain position increment data, where the first compensation model is obtained by training using historical motion state data, and a first fusion module, configured to fuse the position increment data with the positioning data to obtain an error compensation parameter.
Optionally, the second determining unit 603 may include a second input module configured to input the positioning data into a second compensation model in response to the error compensation policy being the second compensation policy to obtain the pseudo-position data, where the second compensation model is obtained by training with the historical positioning data, and a second fusion module configured to fuse the pseudo-position data with the motion state data to obtain the error compensation parameter.
Optionally, the compensation unit 604 may include a third input module for inputting error compensation parameters into the navigation system for compensation.
Optionally, the error compensation device 600 of the navigation system may further comprise a calibration module for calibrating inertial sensors in the navigation system.
In this embodiment, basic data of the navigation system is obtained, wherein the basic data at least comprises positioning data and/or motion state data of a mobile device in the navigation system, the positioning data is used for indicating the position of the mobile device, the motion state data is used for indicating the speed and/or direction of the mobile device, an error compensation strategy of the navigation system is determined based on the basic data, wherein the error compensation strategy is used for indicating a rule for compensating the navigation system, error compensation parameters of the navigation system are determined based on the basic data and the error compensation strategy, and the navigation system is compensated according to the error compensation parameters, wherein an error of the navigation system after compensation relative to the navigation system before compensation is smaller than a target threshold. That is, the invention determines the error compensation strategy and the error compensation parameter according to the basic data of the navigation system, thereby performing error compensation on the navigation system, further solving the technical problem that the navigation system cannot stably operate, and realizing the technical effect of improving the stability of the navigation system.
According to an embodiment of the present invention, there is also provided a computer-readable storage medium including a stored program, wherein the program executes the error compensation method of the navigation system in the method embodiment.
According to an embodiment of the present invention, there is also provided a processor for running a program, where the program runs to execute the error compensation method of the navigation system in the method embodiment.
According to an embodiment of the present invention, there is also provided a computer program product comprising computer instructions which, when executed by a processor, implement the error compensation method of the navigation system in the method embodiment.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of units may be a logic function division, and there may be another division manner in actual implementation, for example, multiple units 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 through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of 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 functional units, 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 functional component 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 Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, etc. which can store the program code.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (10)
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