Adaptive compensation method and system based on integrated navigation

Abstract

The invention relates to the technical field of navigation, and particularly provides a self-adaptive compensation method and system based on integrated navigation, and the method comprises the following steps: obtaining an observed quantity; performing adaptive adjustment on an observation measurement error matrix according to the observation quantity to obtain an adaptive matrix; judging whether the adaptive matrix adjusts, measures and updates the matrix according to the speed, the position and the course to obtain a judgment result; establishing a Kalman filtering process according to the judgment result and the measurement data; obtaining zero offset compensation data according to the Kalman filtering process; and the zero offset compensation data is subjected to a smooth filtering method to obtain an optimal zero offset compensation value. Through Kalman filtering parameters in a self-adaptive matching fusion algorithm, self-adaptive matching is carried out on factors such as wheel speed, an included angle between a carrier and an IMU (Inertial Measurement Unit) and the like according to a speed covariance, a comparison between a combined navigation speed and the wheel speed and the like. The algorithm can be ensured to be suitable for any integrated navigation hardware equipment and various use scenes.

Description

Technical Field

[0001] The invention relates to the field of navigation technology, and in particular to an adaptive compensation method and system based on integrated navigation. Background Art

[0002] Inertial Navigation System (INS) is an important and irreplaceable core sensor module for environment perception and navigation in the field of ADAS and autonomous driving. Its three-dimensional attitude information output and relative and absolute position calculation do not rely on any external equipment or require any external signals. It can be installed in hidden locations such as the chassis of the car to avoid electronic or mechanical attacks. INS can verify the self-consistency of GNSS results and filter and correct the absolute positioning data that is not self-consistent. INS can also provide sub-meter positioning accuracy for several seconds after the GNSS signal disappears, which can buy precious time for autonomous driving vehicles to handle exceptions. Similarly, INS can also perform track deduction on the results of relative positioning when relative positioning fails, and maintain the accuracy of relative positioning for a period of time; for example, when the lane recognition module fails, the road information perceived before the failure and the INS's calculation of the car's track can still allow the car to continue driving in the lane. It can be said that INS is the last line of defense in autonomous driving. The key technology of INS lies mainly in the design and implementation of the algorithm.

[0003] The basic process of the current mainstream combined navigation algorithm is to obtain six-axis angular velocity and acceleration information from the IMU, perform strapdown inertial navigation updates, and then obtain position, velocity, and heading information from the GNSS module as observation quantities for Kalman filter updates. At the same time, when the GNSS is locked, the vehicle CAN information is used for fusion. This method is suitable for general vehicle-mounted inertial navigation when the IMU performance is good (zero bias, random walk). However, the application method is quite rigid and cannot give full play to the performance of GNSS, IMU, and CAN.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] To this end, the technical problem to be solved by the present invention is the basic process of the current mainstream combined navigation algorithm, which obtains six-axis angular velocity and acceleration information from the IMU, performs strapdown inertial navigation update, and then obtains position, speed, and heading information from the GNSS module as observation quantities, and performs Kalman filter update. At the same time, when the GNSS is unlocked, the vehicle body CAN information is used for fusion. This method is applicable to general vehicle-mounted inertial navigation when the IMU performance is good (zero bias, random walk). However, the application method is quite rigid and cannot give full play to the performance of GNSS, IMU, and CAN.

[0006] In order to solve the above technical problems, a first aspect of the present invention provides an adaptive compensation method based on integrated navigation, the method comprising:

[0007] Obtain observations;

[0008] Adaptively adjusting the observation error matrix according to the observation to obtain an adaptive matrix;

[0009] Determine whether the adaptive matrix includes speed, position, and heading adjustment measurement update matrices to obtain a determination result;

[0010] Establishing a Kalman filtering process according to the judgment result and the measurement data;

[0011] Obtaining zero bias compensation data according to the Kalman filtering process;

[0012] The zero bias compensation data is smoothed and filtered to obtain an optimal zero bias compensation value.

[0013] In one embodiment of the present invention, the steps before obtaining the observed value include:

[0014] The ARM system is timed through the PPS signal to make the timestamps of the IMU information and CAN information consistent.

[0015] In one embodiment of the present invention, the step of determining whether the adaptive matrix includes a speed, position, and heading adjustment measurement update matrix and obtaining the determination result comprises:

[0016] Performing identification on the adaptive matrix;

[0017] If there is a preset mark, the judgment result includes updating of the measurement data;

[0018] If there is no preset mark, the determination result is that the measurement data update is not included.

[0019] In one embodiment of the present invention, the step of performing Kalman filtering on the measurement data according to the judgment result to obtain zero bias compensation data includes:

[0020] If the judgment result includes measurement data update, a Kalman filter process is established according to the judgment result and the observation data.

[0021] In one embodiment of the present invention, the step of establishing a Kalman filter process according to the judgment result and the measurement data includes:

[0022] Get wheel speed;

[0023] The wheel speed is used as a state variable and the measurement data is used as a backup observation variable to establish the Kalman filter process.

[0024] In one embodiment of the present invention, the step of obtaining the optimal zero bias compensation value by smoothing filtering the zero bias compensation data further comprises:

[0025] Screening the zero bias compensation data to obtain abnormal data;

[0026] The abnormal data is processed according to the smoothing filtering method to obtain an optimal zero bias compensation value.

[0027] In one embodiment of the present invention, the observed quantities include position, speed and heading.

[0028] The second aspect of the present invention provides an adaptive compensation system based on integrated navigation, the system comprising: a GNSS observation accuracy assessment module, a CAN information accuracy assessment module and an IMU zero bias compensation module;

[0029] The GNSS observation accuracy assessment module is configured to: obtain observations; adaptively adjust the observation error matrix according to the observations to obtain an adaptive matrix;

[0030] The CAN information accuracy assessment module is configured to: determine whether the adaptive matrix includes speed, position, and heading adjustment measurement update matrices to obtain a determination result; establish a Kalman filter process based on the determination result and the measurement data;

[0031] The IMU zero bias compensation module is configured to: obtain zero bias compensation data according to the Kalman filtering process; and obtain an optimal zero bias compensation value by using a smoothing filtering method for the zero bias compensation data.

[0032] The third aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the first aspect or any possible implementation of the first aspect is implemented.

[0033] A fourth aspect of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect or any possible implementation of the first aspect.

[0034] The above technical solution of the present invention has the following advantages compared with the prior art:

[0035] The adaptive compensation method and system based on integrated navigation described in the present invention adaptively matches the Kalman filter parameters in the fusion algorithm according to the covariance accuracy, confidence, etc. of the GNSS module, IMU information, and CAN information of the integrated navigation system, and adaptively matches the factors such as wheel speed, carrier and IMU angle according to speed covariance, integrated navigation speed and wheel speed comparison, etc. It can ensure that the algorithm is applicable to any integrated navigation hardware equipment and various usage scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings.

[0037] Figure 1 It is a flow chart of an adaptive compensation method and system based on integrated navigation provided by the present invention;

[0038] Figure 2 It is a design framework diagram of an adaptive compensation method and system based on integrated navigation provided by the present invention;

[0039] Figure 3 It is a schematic diagram of an optimal fusion strategy of combined navigation based on an adaptive compensation method and system provided by the present invention;

[0040] Figure 4 It is a flow chart of an adaptive compensation method based on integrated navigation and integrated navigation initialization in the system provided by the present invention;

[0041] Figure 5 The present invention provides an adaptive compensation method based on integrated navigation and a flowchart of strapdown inertial navigation update in the system;

[0042] Figure 6 It is a system architecture diagram of an adaptive compensation method and system based on combined navigation provided by the present invention. DETAILED DESCRIPTION

[0043] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0044] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0045] Reference Figure 1 As shown, the present invention provides an adaptive compensation method based on integrated navigation, the method comprising:

[0046] S100, obtaining observations;

[0047] In step S100, the observed values ​​include position, speed and heading. The steps before obtaining the observed values ​​include: timing the ARM system through the PPS signal to make the timestamps of the IMU information and the CAN information consistent.

[0048] In actual application scenarios, refer to Figure 2 and Figure 3 As shown, the GNSS and CAN information in the cache are searched to match the data closest to the IMU data. If there is no match, the Kalman measurement update will not be performed. Due to the data transmission delay of the GNSS module, the PPS output by the GNSS module is used to synchronize the ARM system where the algorithm is located to ensure that the system time accuracy is 30ns. Based on the IMU data, the GNSS data and CAN information with the smallest time difference are searched for matching, and the Kalman filter measurement update is performed. If it is not found due to an abnormality, the measurement update will not be performed.

[0049] The actual observed values ​​in this application include the position, speed, heading transmitted by the GNSS module, and the wheel speed, gear position, etc. in the CAN information. At the same time, according to the gyro data and accelerometer data of the IMU, the zero speed detection algorithm (ZUPT) is used to obtain the speed, and the zero angular rate detection algorithm (ZARU) is used to obtain the heading. The combined navigation fusion algorithm is performed based on the accuracy information of the above observations.

[0050] GNSS, or Global Navigation Satellite System, directly obtains observations by receiving satellite signals, such as the distance from the satellite to the receiver, carrier phase, etc. Among them, the observations include position, velocity, and angular velocity. The covariance matrix of these observations provides information about the correlation and uncertainty between the observations, which is of great significance for improving the accuracy and reliability of GNSS positioning. The purpose of accurately timing the ARM system through the pulse per second (PPS) signal is to ensure that the timestamps carried by the inertial measurement unit (IMU) information and the controller area network (CAN) information are consistent. Doing so will help to associate and integrate information from different sources based on a unified and accurate time reference in subsequent data processing and analysis, thereby improving the overall performance of the system and the validity of the data.

[0051] Reference Figure 4 As shown, the combined navigation initialization specifically includes:

[0052] S110, the sampling period of the sensor SINS.ts, SINS.nts is the sampling period obtained by the algorithm processing;

[0053] S120, SINS attitude initial alignment calculation StartCoarseAlign, pitch angle, roll angle calculation, heading angle compensation according to RTK orientation angle + installation angle, finally calculate four elements according to attitude, and calculate conversion matrix Cb2n according to four elements;

[0054] S130, obtaining the current position SINS.pos (latitude, longitude, elevation) and speed (east, north, day) of the navigation system through the RTK solution of GPS;

[0055] S140, initialize gyro bias SINS.eb, add zero bias SINS.db, navigation system acceleration SINS.an, load system addition table SINS.fb_ib, navigation system force SINS.fn, angular velocities SINS.web and SINS.wnb of the earth coordinate system and navigation coordinate system;

[0056] S150, initializing Earth_Init and updating Earth_UP of geodetic data, mainly inputting navigation system speed and longitude, latitude and elevation, and calculating parameters of navigation coordinate system relative to geodetic coordinate system;

[0057] S160, initializing the position update matrix MPV according to the geodetic coordinate parameters;

[0058] S170, initializing lever arm compensation, and returning the speed and position of the antenna phase center to the carrier coordinate system Lever.

[0059] S200, adaptively adjusting the observation error matrix according to the observation to obtain an adaptive matrix;

[0060] In step S200, firstly, the errors in the covariance regarding position, speed and attitude are used to adaptively adjust the observation error matrix. This means that according to the errors in these different aspects, the error matrix used to describe the uncertainty of the observation is dynamically changed, so as to more accurately reflect the actual measurement situation and improve the accuracy of subsequent calculations and analysis. Secondly, for those observations that cannot directly obtain the covariance output of GNSS (Global Navigation Satellite System), multiple devices are tested on the actual road at the same time. Then, the principles of statistics are used to analyze and process the collected data to estimate the error matrix of these observations. In this way, even in the absence of direct covariance information, the degree of uncertainty of the observation can be roughly determined by statistical methods.

[0061] S300, determining whether the adaptive matrix includes a speed, position, and heading adjustment measurement update matrix, and obtaining a determination result;

[0062] In step S300, it is determined whether the adaptive matrix includes speed, position, and heading adjustment measurement update matrices, and the step of obtaining the determination result includes: identifying the adaptive matrix; if there is a preset identification, the determination result includes measurement data update; if there is no preset identification, the determination result does not include measurement data update. The step of performing Kalman filtering on the measurement data according to the determination result to obtain zero bias compensation data includes: if the determination result includes measurement data update, establishing a Kalman filtering process according to the determination result and the observation data.

[0063] In actual application scenarios, it is determined whether the adaptive matrix contains speed, position, and heading adjustment measurement update matrices. The specific operation is to identify the preset identifier of the adaptive matrix. If the preset identifier is found, it means that the adaptive matrix contains measurement data updates, and the judgment result is that the measurement data updates are included; if not found, it means that the adaptive matrix does not contain measurement data updates, and the judgment result is that the measurement data updates are not included. Based on the judgment result of the previous step, the speed measurement data is processed by Kalman filtering to obtain zero bias compensation data. Specifically: when the judgment result of the previous step is that the measurement data update is included, the Kalman filtering process will be established based on this result and the observed data, so as to achieve further processing and optimization of the data.

[0064] S400, establishing a Kalman filter process according to the judgment result and the measurement data;

[0065] In step S400, the step of establishing a Kalman filter process according to the judgment result and the measurement data includes: acquiring the wheel speed; using the wheel speed as a state quantity and the measurement data as a backup observation quantity for establishing the Kalman filter process.

[0066] In actual application scenarios, the process of establishing a Kalman filter includes two key actions: one is to obtain the wheel speed; the other is to determine the state quantity and observation quantity in the Kalman filter. Among them, the acquired wheel speed is set as the state quantity, and the measurement data involved in the previous judgment result is set as the observation quantity. This setting is to use the wheel speed information and speed measurement data to perform subsequent data processing and optimization under the framework of Kalman filtering to achieve certain specific goals, such as improving measurement accuracy and reducing errors. In short, it is clear that in the process of establishing the Kalman filter, the wheel speed and measurement data must be used, and they are respectively given specific roles in the filtering process (state quantity and observation quantity).

[0067] The adaptive Kalman filtering process used in this application mainly refers to adjusting the observation error matrix R according to the new information updated by the Kalman measurement (the difference between the actual observation value and the predicted value). At the same time, a smoothing filtering method is used for position update and zero bias compensation of IMU data to ensure continuous smooth position and stable and reliable IMU zero bias. The wheel speed of CAN is evaluated and a model is established by relying on the high-precision output speed. It can be guaranteed that the algorithm is applicable to any combined navigation hardware equipment, various types of vehicles and various usage scenarios.

[0068] In the case of Kalman filter initialization:

[0069] Q matrix initialization:

[0070] The system state ΔX is estimated as Also know the error variance matrix

[0071] Initialization of P matrix:

[0072]

[0073] Reference Figure 5 As shown in the figure, when the strapdown inertial navigation is updated:

[0074] S410, correcting the sensor data using the zero bias estimation values ​​SINS.eb and SINS.db calculated in the previous epoch, and assigning them to SINS.wb_ib and SINS.fb_ib;

[0075] S420, geodetic data update Earth_UP, mainly inputting navigation system speed and latitude, longitude and elevation, and calculating parameters of navigation coordinate system relative to geodetic coordinate system;

[0076] S430, calculating the current paddling error compensation according to the geodetic coordinate parameters, and updating the speed, position and attitude;

[0077] S440, Qnb2Cnb calculates the conversion matrix SINS.Cb2n from the carrier to the navigation system according to the attitude four elements SINS.qnb in the navigation system. At the same time, 50 sets of inertial navigation attitude, position, velocity, four elements, angular velocity, and Mpv matrix are cached.

[0078] In the case of time update of the Kalman filter:

[0079] The state matrix is ​​a 15*15 matrix, which is attitude, velocity, position, angular velocity, and acceleration.

[0080]

[0081] X k，k-1 =Φ k，k-1 X k-1 ;

[0082] P k，k-1 =Φ k，k-1 P k-1 Φ k，k-1 T +Q;

[0083] In case of Kalman filter measurement update:

[0084] Kalman measurement update uses the optimal strategy combination in the figure above, using position, speed, and heading. The algorithm flow is to first calculate P k，k-1 H k T , and then calculate H k P k，k-1 H k T +R k , adaptive R matrix, and then inverse (H k P k，k-1 H k T +R k ) -1 , find the filter gain matrix K k =P k，k-1 H k T (H k P k，k-1 H k T +R k )-1 , measurement update X k =X k，k-1 +K k (Z k -H k X k，k-1 ), and finally update P matrix k =P k，k-1 -K k H k P k，k-1 .

[0085] S500, obtaining zero bias compensation data according to the Kalman filtering process;

[0086] In step S500, first, the Kalman filter process is established, and this process is constructed by the above method. Then, the input related data is processed and analyzed based on this specific Kalman filter process, and the final result is to generate zero bias compensation data. "Zero bias compensation data" is usually used to correct or offset the zero bias error in a certain system or measurement to improve the accuracy and reliability of the measurement. In general, the constructed Kalman filter process is used to obtain relevant data that can be used to compensate for the zero bias problem.

[0087] After the Kalman filter is processed, the data is fed back to output the position, speed, and attitude update. The position information and IMU zero bias are smoothed according to the accuracy and the cached previous position and zero bias information, and the smoothed position and stable IMU zero bias are output.

[0088] S600: Obtaining an optimal zero bias compensation value from the zero bias compensation data by a smoothing filtering method.

[0089] In step S600, the step of obtaining an optimal zero bias compensation value from the zero bias compensation data by a smoothing filtering method further includes: screening the zero bias compensation data to obtain abnormal data; and processing the abnormal data according to the smoothing filtering method to obtain an optimal zero bias compensation value.

[0090] In actual application scenarios, screening the zero bias compensation data to obtain abnormal data means checking and distinguishing the existing zero bias compensation data, and selecting the data that does not conform to the normal pattern or law. These selected data are identified as abnormal data. The abnormal data is processed according to the smoothing filtering method to obtain the optimal zero bias compensation value, which means that the specific processing method of smoothing filtering mentioned above is used to perform further operations and calculations on the screened abnormal data. After such processing, the optimal zero bias compensation value can be finally obtained. This optimal value is the most ideal and most accurate value for compensating zero bias after screening abnormal data and processing with a specific method. In short, in the process of obtaining the optimal zero bias compensation value, it is necessary to first select the abnormal data in the zero bias compensation data and process it with a smoothing filtering method to obtain the final desired result.

[0091] The combined fusion method adopted in this application uses IMU data as the state quantity, and the main fusion method is based on the observation quantity, including the fusion of: GNSS RTK position fixed solution data, floating point data, pseudo-range differential data, single-point positioning data, speed information, dual-antenna heading information, etc.; wheel speed, gear position and other information in CAN information. The process of optimizing the fusion method is based on the available observation quantity information and its accuracy in the current scene, and the error estimation of the current state quantity.

[0092] Reference Figure 6 As shown, the second aspect of the present invention provides an adaptive compensation system based on integrated navigation, the system comprising: a GNSS observation accuracy assessment module 100, a CAN information accuracy assessment module 200 and an IMU zero bias compensation module 300;

[0093] The GNSS observation accuracy assessment module 100 is configured to: obtain observations; adaptively adjust the observation error matrix according to the observations to obtain an adaptive matrix;

[0094] The CAN information accuracy evaluation module 200 is configured to: determine whether the adaptive matrix includes the speed measurement data of the RTK, and obtain a determination result; establish a Kalman filter process according to the determination result and the speed measurement data;

[0095] The IMU zero bias compensation module 300 is configured to: obtain zero bias compensation data according to the Kalman filtering process; and obtain an optimal zero bias compensation value from the zero bias compensation data through a smoothing filtering method.

[0096] The effects of the above-mentioned system when applying the above-mentioned method can be found in the description of the above-mentioned method embodiment, which will not be repeated here.

[0097] The third aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the first aspect or any possible implementation of the first aspect is implemented.

[0098] A fourth aspect of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect or any possible implementation of the first aspect.

[0099] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0100] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0101] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0102] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0103] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. An adaptive compensation method based on integrated navigation, characterized in that: The method comprises: Obtain observations; Adaptively adjusting the observation error matrix according to the observation to obtain an adaptive matrix; Determine whether the adaptive matrix includes speed, position, and heading adjustment measurement update matrices to obtain a determination result; Establishing a Kalman filtering process according to the judgment result and the measurement data; Obtaining zero bias compensation data according to the Kalman filtering process; The zero bias compensation data is smoothed and filtered to obtain an optimal zero bias compensation value.

2. The adaptive compensation method based on integrated navigation according to claim 1, characterized in that: The steps before obtaining observations include: The ARM system is timed through the PPS signal to make the timestamps of the IMU information and CAN information consistent.

3. The adaptive compensation method based on integrated navigation according to claim 1, characterized in that: The step of determining whether the adaptive matrix includes a speed, position, and heading adjustment measurement update matrix, and obtaining the determination result comprises: Performing identification on the adaptive matrix; If there is a preset mark, the judgment result includes updating of the measurement data; If there is no preset mark, the determination result is that the measurement data update is not included.

4. The adaptive compensation method based on integrated navigation according to claim 3, characterized in that: The step of performing Kalman filtering on the measurement data according to the judgment result to obtain zero bias compensation data comprises: If the judgment result includes measurement data update, a Kalman filter process is established according to the judgment result and the observation data.

5. The adaptive compensation method based on integrated navigation according to claim 1, characterized in that: The step of establishing a Kalman filter process according to the judgment result and the measurement data comprises: Get wheel speed; The wheel speed is used as a state variable and the measurement data is used as a backup observation variable to establish the Kalman filter process.

6. The adaptive compensation method based on integrated navigation according to claim 1, characterized in that: The step of obtaining the optimal zero bias compensation value by smoothing filtering the zero bias compensation data also includes: Screening the zero bias compensation data to obtain abnormal data; The abnormal data is processed according to the smoothing filtering method to obtain an optimal zero bias compensation value.

7. The adaptive compensation method based on integrated navigation according to claim 1, characterized in that: The observed quantities include position, velocity and heading.

8. An adaptive compensation system based on integrated navigation, characterized in that: The system comprises: a GNSS observation accuracy assessment module, a CAN information accuracy assessment module and an IMU zero bias compensation module; The GNSS observation accuracy assessment module is configured to: obtain observations; adaptively adjust the observation error matrix according to the observations to obtain an adaptive matrix; The CAN information accuracy assessment module is configured to: determine whether the adaptive matrix includes speed, position, and heading adjustment measurement update matrices to obtain a determination result; establish a Kalman filter process based on the determination result and the measurement data; The IMU zero bias compensation module is configured to: obtain zero bias compensation data according to the Kalman filtering process; and obtain an optimal zero bias compensation value by using a smoothing filtering method for the zero bias compensation data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the adaptive compensation method based on combined navigation as claimed in any one of claims 1 to 7 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the adaptive compensation method based on integrated navigation as claimed in any one of claims 1 to 7 are implemented.

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