Imu installation angle adaptive fusion method and system based on integrated navigation innovation

Abstract

This invention provides an adaptive fusion method and system for IMU mounting angle based on integrated navigation information, comprising: establishing a mathematical model of multi-coordinate system and attitude transformation of the vehicle-mounted IMU, defining the vehicle coordinate system, IMU coordinate system and NE-GY navigation coordinate system, and constructing the attitude matrix correlation relationship; establishing a static gravity field vector solution model for the vehicle, utilizing the characteristic that the accelerometer is only sensitive to the gravity vector in a static state, accurately solving the IMU roll and pitch mounting angles under the condition of no small angle approximation; obtaining the spatial displacement vector through inertial navigation attitude velocity integration recursion, solving the horizontal heading mounting angle, and comparing it with the average acceleration vector angle method to achieve mutual verification of calibration results; relying on attitude time series convergence discrimination and working condition reliability screening, obtaining the single effective mounting angle estimate, and using the solution reliability index and navigation information to complete the online adaptive iterative correction of the three-axis mounting angle.

Description

Technical Field

[0001] This invention relates to the field of vehicle-mounted integrated navigation technology, and in particular to an IMU installation angle adaptive fusion method and system based on integrated navigation information. Background Technology

[0002] As the core sensing component of an inertial measurement unit (IMU) in an vehicular inertial navigation system (INS), it often works in conjunction with a Global Navigation Satellite System (GNSS) to form a GNSS-INS integrated navigation system. This integrated system, by fusing angular motion parameters collected by gyroscopes and linear motion information obtained by accelerometers, can calculate the vehicle's attitude, position, and motion state in real time with high precision, playing an indispensable role in key areas such as autonomous driving and special vehicle positioning and navigation.

[0003] GNSS provides absolute position and velocity references, effectively suppressing the cumulative effect of IMU measurement errors. Meanwhile, the IMU possesses independent navigation capabilities; even when GNSS signals fail due to obstruction or interference, the IMU can still output high-precision navigation data within a short time. A key prerequisite for efficient fusion of these two systems is ensuring the uniformity of the data spatial reference. The IMU's mounting angle relative to the vehicle coordinate system is a core parameter affecting the performance of the integrated navigation system. Deviations in the mounting angle directly lead to projection distortion of the physical quantities output by the IMU during coordinate system transformation. For example, a deviation in the heading mounting angle will cause the heading data measured by the IMU to mismatch with the vehicle's actual driving direction, resulting in a mismatch between GNSS position correction information and IMU motion prediction results. Errors in the roll and pitch mounting angles weaken the accelerometer's accuracy in sensing gravity components and motion acceleration, further causing attitude drift and accumulated position estimation errors, ultimately significantly reducing the stability and navigation accuracy of the GNSS-INS integrated navigation system. Therefore, after the IMU is installed on the vehicle, its installation angle must be accurately calibrated to ensure the consistency of the data spatial reference and provide strong support for reliable navigation under complex working conditions.

[0004] Commonly used methods include: (1) External reference fusion method This method uses the standard GNSS onboard equipment as a base reference, and additionally deploys hardware such as wheel speed odometers and visual sensors. Through data fusion algorithms such as Kalman filtering and extended Kalman filtering, it establishes an error model between IMU measurements and external reference information (such as GNSS positioning results and odometer speed data), and then inversely calculates the roll, pitch, and yaw angles. Its core logic is to utilize the absolute reference characteristics of external sensors to correct the relative measurement deviation of the IMU, thereby achieving high-precision estimation of the installation angle.

[0005] (2) Displacement recursion method for IMU stationary-start To avoid relying on additional hardware, a method has emerged that relies solely on IMU data to solve for the three-axis mounting angles. First, using IMU data during the vehicle's stationary phase, the roll and pitch mounting angles are calculated based on the projected geometric relationship between the accelerometer output and the gravity vector. Then, leveraging the vehicle's linear acceleration from a standstill to start, the horizontal acceleration component output by the IMU is integrated twice to recursively calculate the vehicle's trajectory vector. Finally, the heading mounting angle is calculated using the angle between the trajectory vector's direction and the IMU's forward measurement axis. The core logic relies on the motion characteristics of a stationary start with an initial velocity of 0, using the uniqueness of the displacement trajectory direction to estimate the heading angle, ultimately achieving a complete solution for the three-axis mounting angles without external hardware assistance.

[0006] Both of the aforementioned existing technologies have significant limitations and are difficult to adapt to the actual needs of complex in-vehicle scenarios: (1) Deficiencies of external reference fusion method Although GNSS is a standard module in vehicle navigation, this method requires the additional deployment of hardware such as wheel speed odometers and visual sensors. This not only increases the hardware procurement cost of the vehicle, but also requires additional installation and debugging (such as matching the odometer with the vehicle wheel diameter and calibrating the visual sensor), increasing the complexity of system integration. At the same time, its performance is heavily dependent on the quality of GNSS signals—in scenarios where GNSS signals are blocked or fail, such as tunnels, underground parking garages, and densely populated areas with tall buildings, external reference information is interrupted, and the installation angle estimation will completely fail, making it impossible to meet the calibration requirements in complex environments.

[0007] (2) Defects of the IMU static-start displacement recursion method While this method requires no additional hardware and can completely solve for the three-axis mounting angles, it suffers from stringent application limitations and significant technical shortcomings: The method must strictly start the recursive calculation from a stationary vehicle state. The acceleration during the initial starting phase may be too small, and the weak acceleration signal is easily masked by sensor noise. To obtain an effective and identifiable displacement trajectory, a lengthy integration recursive calculation is required. This not only results in a cumbersome calculation process and a large computational load but also introduces more accumulated errors due to the increased integration time, leading to distortion of the displacement trajectory and a significant increase in the estimation error of the heading mounting angle. Furthermore, the calculated mounting angles are only for single-use and do not consider how to weight and fuse multiple sets of mounting angles, resulting in low system reliability.

[0008] In summary, existing technologies generally suffer from the following drawbacks: high hardware costs, poor adaptability to various scenarios, long inertial navigation recursion times, easy accumulation of errors, and lack of consideration for weighted fusion of multiple installation angles. These limitations make it difficult to meet the needs of practical applications. Summary of the Invention

[0009] This invention provides an adaptive fusion method and system for IMU mounting angle based on integrated navigation information, which addresses the shortcomings of existing technologies such as high hardware costs, poor scenario adaptability, long inertial navigation recursion time, easy error accumulation, and lack of consideration for weighted fusion of multiple mounting angles. By calculating the roll and pitch mounting angles when stationary, and calculating the heading mounting angle through velocity increments and attitude constraints, the adaptive fusion and update of the three mounting angles is achieved. This method is suitable for vehicle-mounted inertial navigation calibration scenarios, eliminates the need for external references such as odometers, and significantly simplifies the implementation conditions of mounting angle calibration.

[0010] In a first aspect, the present invention provides an IMU installation angle adaptive fusion method based on integrated navigation information, comprising: A mathematical model for vehicle-mounted IMU multi-coordinate system and attitude transformation is established, defining the vehicle coordinate system, IMU coordinate system and NE-G ground navigation coordinate system, and constructing the attitude matrix correlation relationship; A static gravity field vector calculation model for the vehicle is established. Taking advantage of the characteristic that the accelerometer is only sensitive to the gravity vector in a static state, the roll and pitch installation angles of the IMU are accurately calculated without small angle approximations. The spatial displacement vector is obtained by recursively integrating the attitude velocity of the inertial navigation system. The horizontal heading angle is then calculated and compared with the mean acceleration vector angle method to achieve mutual verification and validation of the calibration results. Based on attitude timing convergence discrimination and working condition reliability screening, the estimated effective installation angle for a single operation is obtained. By utilizing the solution reliability index and navigation information, the online adaptive iterative correction of the three-axis installation deflection angle is completed.

[0011] According to the present invention, an IMU installation angle adaptive fusion method based on integrated navigation information is provided, which establishes a mathematical model of vehicle-mounted IMU multi-coordinate system and attitude transformation, defines the vehicle coordinate system, IMU coordinate system and NE-GYN navigation coordinate system, and constructs the attitude matrix correlation relationship, including: The attitude matrix of the IMU relative to the navigation system is obtained by simultaneously combining the attitude matrix of the vehicle body relative to the navigation system and the attitude matrix of the IMU relative to the vehicle body mounting.

[0012] In the formula, The attitude matrix of the IMU relative to the navigation frame. This is the vehicle's attitude matrix relative to the navigation system. This is the mounting attitude matrix of the IMU relative to the vehicle body.

[0013] According to the present invention, an IMU installation angle adaptive fusion method based on integrated navigation information is provided, which establishes a vehicle static gravity field vector calculation model and utilizes the characteristic that the accelerometer is only sensitive to the gravity vector in a static state, including: The vehicle is positioned horizontally, without slope or vibration interference, and the vehicle coordinate system is completely aligned with the local horizontal navigation coordinate system. The expression for the gravity vector in the navigation coordinate system is:

[0014] Under static conditions, the IMU accelerometer outputs a specific force vector. Ignoring sensor bias and random noise, the coordinate transformation relationship is satisfied: .

[0015] According to the present invention, an IMU installation angle adaptive fusion method based on integrated navigation innovation accurately calculates the IMU roll and pitch installation angles without small angle approximations, including: The approximate strict global attitude matrix with no small angles corresponding to the IMU's roll and pitch mounting attitudes relative to the vehicle body:

[0016] Normalize the static comparison data:

[0017] By combining the geometric relationships of gravity vector projection and rigorous attitude matrix derivation, we obtain accurate formulas for calculating the roll and pitch installation angles without angular constraints:

[0018] .

[0019] According to the present invention, an IMU installation angle adaptive fusion method based on integrated navigation information is provided, which obtains the spatial displacement vector through inertial navigation attitude velocity integration and solves the horizontal heading installation angle, including: The inertial navigation attitude is recursively derived through quaternion attitude update. The attitude update differential equation in the navigation coordinate system is:

[0020] In the formula, The angular velocity vector of the relative navigation frame observed by the IMU. This refers to quaternion multiplication operations; The attitude matrix of the IMU relative to the navigation frame can be obtained by updating the quaternion in real time. ; The inertial navigation velocity update formula under the navigation system is:

[0021] Integrating the velocity in the time domain yields the IMU's displacement vector in the navigation frame:

[0022] Transform the navigation system displacement vector to the IMU carrier coordinate system:

[0023] When the vehicle is traveling in a straight line, its actual trajectory is strictly along the positive X-axis. Therefore, the ideal displacement vector of the vehicle has only a longitudinal component, and the lateral displacement is zero.

[0024] Due to the existence of the heading installation angle The displacement vector calculated by the IMU has a fixed horizontal angle relative to the ideal displacement vector of the vehicle body. When only the heading deviation is retained, the rotation transformation matrix between the IMU and the vehicle body on the horizontal plane is:

[0025] Displacement vectors satisfy coordinate transformation relationships Expanding the two-dimensional components of the horizontal plane yields:

[0026] Eliminate displacement amplitude The principal value of the heading installation angle, obtained by resolving the inertial navigation displacement vector, is obtained without any approximations: .

[0027] According to the present invention, an IMU installation angle adaptive fusion method based on integrated navigation information is provided, which combines the average acceleration vector angle method for comparison to achieve mutual verification and validation of calibration results, including: Under the condition of smooth, straight-line driving, the vehicle body has no continuous lateral motion, and the average acceleration of the vehicle body is strictly along the X-axis direction of travel. The ideal average acceleration vector of the vehicle body is:

[0028] Multiple frames of IMU acceleration data were collected during dynamic driving, and the average acceleration vector in the IMU coordinate system was obtained after mean filtering.

[0029] Roll and pitch deviations have been fully compensated, and the acceleration vector is only affected by the horizontal heading angle. The coordinate transformation satisfies:

[0030] Expanding the horizontal plane constraints, and considering the characteristic that the lateral average acceleration is 0 in the straight-line driving condition. The constraint equations are obtained as follows:

[0031] The heading installation angle verification value obtained by the acceleration vector matching method is obtained by solving:

[0032] The consistency of the results was verified and calibrated using the inertial navigation recursive method and the average acceleration vector matching method, respectively.

[0033] According to the present invention, an IMU installation angle adaptive fusion method based on integrated navigation innovation is provided, including heading installation angle sequence convergence determination and installation angle confidence verification, comprising: Get the current epoch Including the recent Each epoch, constructing the heading installation angle sequence. Set the heading angle deviation threshold between adjacent epochs. A sequence is considered convergent if it simultaneously satisfies the following constraints:

[0034] Set horizontal attitude perturbation threshold Heading and turning disturbance threshold Let the epoch interval of the convergent sequence be... The quantification constraints are:

[0035] In the formula, For the epoch interval corresponding to the convergence sequence of the heading installation angle; for The sum of variances of the roll and pitch angle sequences within the interval quantifies the overall perturbation of the horizontal attitude. for The total heading angle within the interval reflects whether the vehicle is maintaining straight-line travel; The variance calculation operator is expressed as follows:

[0036] in, For the interval epoch number, For sequence The mean; , Horizontal reference coordinate system Next The roll and pitch angles of the epoch. For the first in this coordinate system Epoch Gyroscope Effective output value of axis The epoch sampling interval; Construct an installation angle confidence model to assess the reliability of the solution:

[0037] In the formula, For the installation angle confidence, , These are positive weighting coefficients, adjusted according to the degree of influence of the operating condition disturbance; The larger the value, the higher the reliability of the solution; set a confidence threshold. , The solution is deemed invalid and the result is discarded. It is determined to be valid and retained. According to the present invention, an IMU installation angle adaptive fusion method based on integrated navigation innovation is provided, which integrates the final value calculation of the heading installation angle with the three-axis installation angle, including: After the heading installation angle sequence converges and the confidence check passes, The calculated heading and installation angle values ​​for each epoch are filtered using a sliding window mean filter, and then... The mean of each epoch is used as the optimal estimate of the heading angle:

[0038] Roll installation angle calculated using the gravity projection method Pitch installation angle With respect to the above-mentioned optimal heading installation angle By integrating the data, we obtain the three-axis mounting angle vectors of the IMU relative to the vehicle coordinate system: .

[0039] According to the present invention, an adaptive fusion method for IMU installation angle based on integrated navigation information is provided, wherein the adaptive update of the integrated navigation information determination includes: Definition of the first The effective solution result is: ,right First, a validity screening of the operating conditions is performed to ensure that the calculation results correspond to normal driving conditions. The specific judgment criteria are as follows: Preliminary Position Information Assessment: Calculation of Position Information Module Length for GNSS-INS Integrated Navigation ,like ( If the location information threshold is used, then the solution condition is determined to be without obvious abnormalities. Stability determination of consecutive solutions: To verify the consistency of three consecutive valid solutions, the following conditions must be met simultaneously. and , To set an installation angle deviation threshold, ensuring that the calculation results are free from sudden fluctuations; When both of the above conditions are met, the determination is made. , , For fusionable results, define For the first If there are three fusionable results, then these three fusionable results are denoted as... , , Only such fusion-compatible results are retained for subsequent weighted fusion; Using the confidence level of each set of mounting angles as the weight, the weighted fusion of multiple sets of fusionable mounting angles is completed in two steps: Initial fusion: After accumulating 3 sets of fusionable results, initial fusion is triggered to build a stable baseline. The fusion formula is as follows:

[0040] In the formula, This represents the total number of fusionable results; for The installation angle is obtained by weighted fusion of the fusion results; This is used as the cumulative confidence weight; this step effectively avoids random disturbances in single-batch calculations by redundantly fusing multiple sets of data. Dynamic iterative fusion: After the initial fusion is completed, each additional set of valid solution results selected by the operating conditions is further fused. ,determination If this condition is met, lightweight iterative fusion will be initiated, eliminating the need to store historical data. The iterative formula is as follows:

[0041] In the formula, For the first The mounting angle after secondary fusion; For the front The cumulative confidence weight of the sub-fusion This is the updated cumulative weight; Outlier detection and adaptive updates are performed on the newly calculated installation angle group to eliminate abnormal operating condition deviations: Outlier detection: If the newly added valid solution does not meet the requirements... If it is, then it is judged as an outlier. They will not be included in the fusion process for the time being to avoid affecting the overall estimation accuracy due to a single abnormal operating condition. IMU relocation determination: A physical relocation of the IMU is determined to have occurred if and only if both of the following conditions are met simultaneously: Timing anomaly consistency: Two consecutive sets of solution results are outliers, and satisfy the following conditions: This indicates that the installation angle variation is consistent, thus ruling out random anomalies; Location-based new information evidence: If This indicates that the GNSS-INS integrated navigation position information is outside the normal range, which can further verify that the anomaly was caused by IMU movement; When both conditions are met, the fusion benchmark is updated to the latest valid solution; if only one condition is met, it is judged as a normal anomaly, the abnormal data is removed and the current fusion result remains unchanged. Adaptive stable update: Set a stable threshold for fusion weights If the cumulative confidence weights satisfy If the installation angle is determined to be stable, dynamic updates are stopped and the current fusion value is used; if the condition is not met, a valid new solution group is included in the fusion, and the weighted fusion value is dynamically updated. When the fusion weight Reaching the preset threshold When the fusion result has fully utilized the redundancy of multiple batches of data and the installation angle estimation tends to stabilize, the iterative update is stopped, and the current weighted fusion value is taken as the final optimal estimate of the IMU's three-axis installation angle. .

[0042] Secondly, the present invention also provides an IMU installation angle adaptive fusion system based on integrated navigation information, comprising: The first module is used to establish a mathematical model of the vehicle-mounted IMU's multi-coordinate system and attitude transformation, define the vehicle coordinate system, IMU coordinate system and NE-G navigation coordinate system, and construct the attitude matrix relationship. The second module is used to establish a static gravity field vector calculation model for the vehicle. It utilizes the characteristic that the accelerometer is only sensitive to the gravity vector in a static state to accurately calculate the IMU roll and pitch angles without small angle approximations. The first calculation module is used to recursively obtain the spatial displacement vector through the integral of the inertial navigation attitude velocity, solve the horizontal heading installation angle, and compare it with the average acceleration vector angle method to realize mutual verification and validation of the calibration results. The second calculation module is used to obtain the estimated value of the single effective installation angle based on attitude timing convergence discrimination and working condition confidence screening. It then uses the solution confidence index and navigation information to complete the online adaptive iterative correction of the three-axis installation deflection angle.

[0043] The IMU mounting angle adaptive fusion method and system based on integrated navigation information provided by this invention achieves adaptive fusion update of the three mounting angles by calculating the roll and pitch mounting angles when stationary and calculating the heading mounting angle through velocity increment and attitude constraints. It is suitable for vehicle inertial navigation calibration scenarios, does not require external references such as odometers, and significantly simplifies the implementation conditions of mounting angle calibration. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating the IMU installation angle adaptive fusion method based on integrated navigation information provided by the present invention. Figure 2 This is a schematic diagram of the installation angle of the GNSS / INS integrated navigation vehicle IMU provided by the present invention; Figure 3 This is a schematic diagram of the adaptive update process for the vehicle-mounted IMU installation angle in the integrated navigation information judgment provided by the present invention; Figure 4 This is a schematic diagram of the structure of the IMU installation angle adaptive fusion system based on integrated navigation information provided by the present invention; Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0047] Figure 1 This is a flowchart illustrating the IMU installation angle adaptive fusion method based on integrated navigation information provided in an embodiment of the present invention, as shown below. Figure 1 As shown, it includes: Step 100: Establish a mathematical model for the multi-coordinate system and attitude transformation of the vehicle-mounted IMU, define the vehicle coordinate system, the IMU coordinate system and the NE-G ground navigation coordinate system, and construct the attitude matrix correlation relationship; Step 200: Establish a static gravity field vector calculation model for the vehicle. Utilize the characteristic that the accelerometer is only sensitive to the gravity vector in a static state to accurately calculate the IMU roll and pitch installation angles without small-angle approximations. Step 300: Obtain the spatial displacement vector by recursively integrating the attitude velocity of the inertial navigation system, solve the horizontal heading angle, and compare it with the mean acceleration vector angle method to achieve mutual verification and validation of the calibration results; Step 400: Based on attitude timing convergence discrimination and working condition confidence screening, obtain the estimated value of the single effective installation angle, and use the solution confidence index and navigation information to complete the online adaptive iterative correction of the three-axis installation deflection angle. Specifically, this embodiment of the invention first establishes a mathematical model of the vehicle-mounted IMU's multi-coordinate system and attitude transformation, defining the vehicle coordinate system, the IMU coordinate system, and the NE-G ground navigation coordinate system, and constructing the attitude matrix correlation relationship; then, it establishes a vehicle static gravity field vector solution model, utilizing the characteristic that the accelerometer is only sensitive to the gravity vector in a static state, to accurately solve the IMU roll installation angle and pitch installation angle under the condition of no small angle approximation; the spatial displacement vector is obtained by recursively integrating the inertial navigation attitude velocity, and the horizontal heading installation angle is solved, and compared with the average acceleration vector angle method to achieve mutual verification of calibration results; finally, based on the attitude time series convergence discrimination and the working condition credibility screening, the estimated value of the single effective installation angle is obtained, and the online adaptive iterative correction of the three-axis installation deviation angle is completed by using the solution credibility index and navigation information.

[0048] This invention utilizes the vehicle's linear acceleration to solve for the installation angle and innovatively employs a method of updating the installation angle based on integrated navigation information. This method adaptively fuses and updates the installation angle, resulting in higher system reliability compared to using only a single calculated set of installation angles.

[0049] Based on the above embodiments, step 100 includes: establishing a mathematical model of vehicle-mounted IMU multi-coordinate system and attitude transformation, standardizing the definition of vehicle coordinate system, IMU coordinate system and NE-G ground navigation coordinate system, constructing the attitude matrix correlation relationship, and laying a theoretical foundation for subsequent installation angle calculation.

[0050] Coordinate system definition and attitude transformation fundamental theory To accurately calculate the installation attitude deviation of the IMU relative to the vehicle body, this paper defines a standard coordinate system for vehicle navigation. All installation angles to be calculated are fixed attitude deviations of the IMU carrier coordinate system relative to the vehicle body coordinate system. There are no small angle approximation constraints throughout the process, and it supports calibration at any installation tilt angle.

[0051] Core coordinate system definition: Each coordinate system follows the rules of a right-handed rectangular coordinate system, as defined below: Vehicle body coordinate system (b-frame): X-axis is the vehicle's longitudinal axis, pointing in the direction of travel; Y-axis is the vehicle's lateral axis, pointing to the left side of the vehicle; Z-axis is the vehicle's vertical axis, perpendicular to the ground and vertically upwards. Vehicle attitude angles include roll angle. Pitch angle Heading angle .

[0052] IMU carrier coordinate system (i-system): The IMU's inherent measurement coordinate system, which, due to installation process and bracket fixation, has a fixed installation deviation from the vehicle coordinate system. Among them: roll installation angle. For IMU lateral tilt deviation, pitch installation angle For IMU longitudinal pitch deviation, yaw installation angle This refers to the horizontal yaw deviation of the IMU.

[0053] The Northeast-Eastern Navigation Coordinate System (n-system, NED): a global reference coordinate system with the X-axis pointing north, the Y-axis pointing east, and the Z-axis pointing vertically downwards. It serves as a unified reference for attitude calculation and gravity vector observation.

[0054] like Figure 2 In the schematic diagram of the GNSS / INS integrated navigation vehicle-mounted IMU installation angle shown, the vehicle coordinate system is called the v system, and the carrier (IMU) coordinate system is called the b system. The XYZ axes of the v system point to the lower right front of the vehicle body, while the XYZ axes of the b system do not coincide with the lower right front of the vehicle body, but form an angle. Therefore, it is necessary to construct a rotation matrix from the vehicle system to the IMU system.

[0055] Attitude Transformation Core Relationship The attitude matrix of the IMU relative to the navigation system can be obtained by simultaneously solving the attitude matrix of the vehicle body relative to the navigation system and the attitude matrix of the IMU relative to the vehicle body. The core transformation formula is:

[0056] In the formula: The attitude matrix of the IMU relative to the navigation frame. This is the vehicle's attitude matrix relative to the navigation system. The mounting attitude matrix of the IMU relative to the vehicle body is the core transformation matrix for solving the mounting angle in this paper.

[0057] Based on the above embodiments, step 200 includes: establishing a static gravity field vector calculation model for the vehicle, and utilizing the characteristic that the accelerometer is only sensitive to the gravity vector in a static state to accurately calculate the IMU roll and pitch installation angles without small angle approximations.

[0058] Static solution principle When the vehicle is stationary and horizontal, there is no interference from motion acceleration or angular velocity. The IMU accelerometer is only sensitive to the local pure gravity vector. The gravity vector is a known constant reference vector in the navigation system. The heading rotation in the horizontal plane will not change the gravity vector projection. Therefore, the static gravity field can only calculate the roll and pitch installation angles in the vertical plane and cannot observe the heading installation deviation.

[0059] Calibration prerequisites: The vehicle is horizontally stationary, without slope or vibration interference, and the vehicle coordinate system is completely aligned with the local horizontal navigation coordinate system.

[0060] Gravity vector and attitude transformation model The expression for the gravity vector in the navigation coordinate system is:

[0061] Under static conditions, the IMU accelerometer outputs a specific force vector. Ignoring sensor bias and random noise, the coordinate transformation relationship is satisfied:

[0062] The strict global attitude matrix (without small-angle approximation) corresponding to the IMU's roll and pitch mounting attitude relative to the vehicle body:

[0063] Precise analytical solution for roll and pitch installation angles To eliminate the influence of accelerometer output amplitude, the static force comparison data is normalized:

[0064] By combining the geometric relationship of gravity vector projection and rigorous attitude matrix derivation, we obtain accurate formulas for calculating roll and pitch installation angles without angular constraints:

[0065]

[0066] Static Algorithm Description This method relies solely on static accelerometer data, exhibits no cumulative error, boasts a simple algorithm, high stability, and adaptability to installation tilt deviations of any magnitude. In practical applications, multiple frames of static data need to be collected and mean-filtered to suppress random noise from the sensor and improve the accuracy of the installation angle calibration.

[0067] Based on the above embodiments, step 300 includes: obtaining the spatial displacement vector by recursively integrating the inertial navigation attitude velocity, solving the horizontal heading installation angle, and then comparing it with the average acceleration vector angle method to achieve mutual verification and validation of the calibration results.

[0068] Consistency verification between the heading installation angle solution based on inertial navigation displacement vector and acceleration vector Dynamic solution overall principle Static gravity fields can only observe roll and pitch installation deviations in the vertical plane, but cannot identify the horizontal heading installation angle. This paper innovatively adopts a dual-vector cross-verification calibration strategy: the main algorithm obtains the IMU's three-dimensional displacement vector based on inertial navigation attitude recursion and velocity integration, and accurately calculates the heading installation angle by using the horizontal angle between the measured IMU displacement vector and the ideal forward displacement vector of the vehicle body; the auxiliary verification algorithm uses the dynamic average acceleration vector angle method to solve for the heading installation angle. The two algorithms are physically independent and their principles are not coupled. The high consistency of the results proves that the heading installation angle calibration results are accurate and reliable. All calculations have no small angle approximations and are adaptable to any installation deviation angle.

[0069] Calibration prerequisites: Static calibration and compensation for roll and pitch installation angles have been completed, and the IMU and vehicle body retain only a fixed horizontal heading installation deviation. The vehicle maintains a smooth, constant speed and straight-line driving, without steering, lateral movement, or severe bumps, ensuring that the vehicle's motion vector is strictly along the front of the vehicle body.

[0070] Heading and Installation Angle Master Algorithm Based on Inertial Navigation Displacement Vector Standard inertial navigation system (INS) updates are performed based on IMU gyroscope and accelerometer data. The motion displacement vector in the IMU coordinate system is obtained through attitude calculation, force integration, and velocity integration. The heading and installation angle are then calculated using the spatial pointing deviation of the displacement vector. This method relies on the actual motion trajectory of the carrier, providing intuitive physical meaning and higher calibration accuracy.

[0071] First, the inertial navigation attitude is recursively derived through quaternion attitude update. The differential equation for attitude update in the navigation coordinate system is:

[0072] In the formula: The angular velocity vector of the relative navigation frame observed by the IMU. This involves quaternion multiplication. Real-time updates of the quaternions yield the attitude matrix of the IMU relative to the navigation frame. This enables coordinate system transformation and error compensation for force comparison.

[0073] Inertial navigation velocity update formula (under navigation system):

[0074] Integrating the velocity in the time domain yields the IMU's displacement vector in the navigation frame:

[0075] Transform the navigation system displacement vector to the IMU carrier coordinate system:

[0076] When the vehicle is traveling in a straight line, its actual trajectory is strictly along the positive X-axis. Therefore, the ideal displacement vector of the vehicle has only a longitudinal component, and the lateral displacement is zero.

[0077] Due to the existence of the heading installation angle The displacement vector calculated by the IMU has a fixed horizontal angle relative to the ideal displacement vector of the vehicle body. When only the heading deviation is retained, the rotation transformation matrix between the IMU and the vehicle body in the horizontal plane is:

[0078] Displacement vectors satisfy coordinate transformation relationships Expanding the two-dimensional components of the horizontal plane yields:

[0079] Eliminate displacement amplitude The principal value of the heading installation angle, obtained by resolving the inertial navigation displacement vector, is obtained without any approximations:

[0080] This angle is essentially the angle between the measured displacement vector of the IMU and the ideal positive displacement vector of the vehicle body on the horizontal plane, which is the actual heading installation deviation of the IMU.

[0081] Results Consistency Verification Algorithm Based on Average Acceleration Vector To verify the accuracy of the heading installation angle obtained by the above inertial navigation recursive solution, a completely independent dynamic average acceleration vector angle method is introduced for cross-validation. This method does not rely on gyroscope data and attitude integrals, but only uses accelerometer observations. It has no common source error with the inertial navigation recursive algorithm and can effectively determine whether the calibration results are reliable.

[0082] Under the condition of smooth, straight-line driving, the vehicle body has no continuous lateral motion, and the average acceleration of the vehicle body is strictly along the X-axis direction of travel. The ideal average acceleration vector of the vehicle body is:

[0083] Multiple frames of IMU acceleration data were collected during dynamic driving, and the average acceleration vector in the IMU coordinate system was obtained after mean filtering.

[0084] Roll and pitch deviations have been fully compensated, and the acceleration vector is only affected by the horizontal heading angle. The coordinate transformation satisfies:

[0085] Expanding the horizontal plane constraints, and considering the characteristic that the lateral average acceleration is zero in the straight-line driving condition. The constraint equations can be obtained as follows:

[0086] The heading installation angle verification value obtained by the acceleration vector matching method is obtained by solving:

[0087] Consistency verification and calibration conclusions of dual algorithm results This invention uses two completely independent algorithms to solve for the heading angle: 1) Inertial navigation recursive method: relying on gyroscope attitude integration and trajectory displacement vector to solve, which is the main calibration result; 2) Average acceleration vector matching method: This method relies on the spatial angle characteristics of acceleration vectors to solve the problem and provides independent verification results.

[0088] Under ideal error-free conditions, the solution value of the inertial navigation displacement vector With acceleration vector solution value The values ​​are completely equal; under actual working conditions, affected by sensor noise, minor vehicle vibrations, and slight road surface disturbances, the two values ​​are highly similar with minimal deviation. The displacement vector is solved based on the trajectory motion characteristics, and the acceleration vector is solved based on the force motion characteristics. The consistency of the results from the two independent physical dimensions fully verifies the authenticity and accuracy of the heading installation angle calibration results in this paper, proving that the overall IMU installation angle calibration scheme is reliable and effective.

[0089] Complete calibration process: First, the roll and pitch installation angles are calculated and compensated using a static gravity vector model to completely eliminate vertical plane attitude deviations; second, the displacement vector is obtained by integrating the inertial navigation attitude velocity using the vehicle's straight-line dynamic data, and the principal value of the high-precision heading installation angle is calculated; finally, the independent average acceleration vector angle method is used for cross-validation, and the results of the two vectors corroborate each other to achieve full-dimensional, high-precision self-calibration correction of the vehicle-mounted IMU's three-axis installation angle.

[0090] Based on the above embodiments, step 400 includes: Based on attitude timing convergence discrimination and working condition reliability screening, the estimated effective installation angle for a single operation is obtained. Using the calculated reliability index and navigation information, online adaptive iterative correction of the three-axis installation deflection angle is achieved.

[0091] Heading installation angle sequence convergence determination To filter out measurement noise and random disturbances and improve the accuracy and stability of the heading installation angle calculation, a convergence test is performed on the continuous heading installation angle sequence calculated epoch by epoch. The current epoch is then used. Including the recent Each epoch, constructing the heading installation angle sequence. Set the heading angle deviation threshold between adjacent epochs. A sequence is considered convergent if it simultaneously satisfies the following constraints:

[0092] Installation angle confidence test After the heading and installation angle sequence converges, the confidence level of the installation angles is verified to quantitatively evaluate the degree of horizontal attitude disturbance and heading change, eliminating low-confidence operating conditions. A horizontal attitude disturbance threshold is set. Heading and turning disturbance threshold Let the epoch interval of the convergent sequence be... The quantification constraints are:

[0093] In the formula, For the epoch interval corresponding to the convergence sequence of the heading installation angle; for The sum of variances of the roll and pitch angle sequences within the interval quantifies the overall perturbation of the horizontal attitude (the smaller the variance, the more stable the attitude). for The total heading angle within the interval reflects whether the vehicle is maintaining straight-line travel; The variance calculation operator is expressed as follows:

[0094] ( For the interval epoch number, For sequence (mean) , Horizontal reference coordinate system Next The roll and pitch angles of the epoch. For the first in this coordinate system Epoch Gyroscope Effective output value of axis The epoch sampling interval.

[0095] Construct an installation angle confidence model to assess the reliability of the solution:

[0096] In the formula, For the installation angle confidence, , It is a positive weighting coefficient, which can be adjusted according to the degree of influence of the operating condition disturbance; The larger the value, the higher the reliability of the solution. Set a confidence threshold. , The solution is deemed invalid and the result is discarded. It is determined to be valid and retained.

[0097] Calculation of final value of heading installation angle and integration of three-axis installation angle After the heading installation angle sequence converges and the confidence check passes, The calculated heading and installation angle values ​​for each epoch are filtered using a sliding window mean filter, and then... The mean of each epoch is used as the optimal estimate of the heading angle:

[0098] Roll installation angle calculated using the gravity projection method Pitch installation angle With respect to the above-mentioned optimal heading installation angle By integrating the data, we obtain the three-axis mounting angle vectors of the IMU relative to the vehicle coordinate system:

[0099] Adaptive update of integrated navigation information judgment Collection and filtering of multiple sets of installation angle calculation results Definition of the first The effective solution result is: ,right First, a validity screening of the operating conditions is performed to ensure that the calculation results correspond to normal driving conditions. The specific judgment criteria are as follows: 1. Preliminary Position Information Assessment: Calculation of Position Information Module Length for GNSS-INS Integrated Navigation ,like ( If the location information threshold is used, then the solution condition is determined to be without obvious abnormalities. 2. Stability determination of continuous solutions: To verify the consistency of three consecutive valid solutions, the following conditions must be met simultaneously. and ( (This is the installation angle deviation threshold) to ensure that the solution results are free from sudden fluctuations.

[0100] When both of the above conditions are met, the determination is made. , , This is a fusion-compatible result. Definition For the first If there are three fusionable results, then these three fusionable results are denoted as... , , Only such fusion-compatible results are retained for subsequent weighted fusion.

[0101] Confidence-based weighted fusion computation Using the confidence level of each set of mounting angles as weight, a two-step weighted fusion of multiple sets of fusionable mounting angles is performed to improve the accuracy and robustness of the final mounting angle: 1. Initial Fusion: After accumulating 3 sets of fusionable results, initial fusion is triggered to build a stable baseline. The fusion formula is:

[0102] In the formula, This represents the total number of fusionable results; for The installation angle is obtained by weighted fusion of the fusion results; This is used as the cumulative confidence weight; this step effectively avoids random disturbances in single-batch calculations by redundantly fusing multiple sets of data.

[0103] 2. Dynamic Iterative Fusion: After the initial fusion is completed, for each newly added set of valid solution results selected by the working conditions... , first determine If this condition is met, lightweight iterative fusion will be initiated, eliminating the need to store historical data. The iterative formula is as follows:

[0104] In the formula, For the first The mounting angle after secondary fusion; For the front The cumulative confidence weight of the sub-fusion The updated cumulative weights are used; the adaptive fusion update of the installation angle is achieved through binary iteration, taking into account the reliability of new data and historical fusion results.

[0105] Outlier Detection and Adaptive Update Outlier detection and adaptive updates are performed on the newly calculated installation angle group to eliminate abnormal operating condition deviations: 1. Outlier detection: If the newly added valid solution does not meet the requirements... If it is, then it is judged as an outlier. They will not be included in the fusion process for the time being, in order to avoid affecting the overall estimation accuracy due to a single abnormal operating condition.

[0106] 2. IMU Movement Detection: An IMU is determined to have physically moved if and only if both of the following conditions are met simultaneously: (1) Temporal anomaly consistency: Two consecutive sets of solution results are outliers, and satisfy the following conditions: This indicates that the installation angle variation is consistent, thus ruling out random anomalies; (2) Location-based supporting evidence: If This indicates that the GNSS-INS integrated navigation position information is outside the normal range, which can further verify that the anomaly was caused by IMU movement.

[0107] When both conditions are met, the fusion benchmark is updated to the latest valid solution; if only one condition is met, it is judged as a normal anomaly, the abnormal data is removed and the current fusion result remains unchanged.

[0108] 3. Adaptive Stable Update: Set a stable threshold for fusion weights. If the cumulative confidence weights satisfy If the installation angle is determined to be stable, dynamic updates are stopped and the current fusion value is used; if the condition is not met, a valid new solution group is included in the fusion, and the weighted fusion value is dynamically updated.

[0109] When the fusion weight Reaching the preset threshold When the fusion result has fully utilized the redundancy of multiple batches of data and the installation angle estimation tends to stabilize, the iterative update is stopped, and the current weighted fusion value is taken as the final optimal estimate of the IMU's three-axis installation angle. .

[0110] Thus, the confidence-weighted adaptive fusion update of the IMU mounting angle has been completed. This method mines the redundancy value of multiple batches of data through staged weighted fusion, and improves robustness by combining outlier detection and IMU movement determination. It achieves high-precision and long-term stable estimation of the mounting angle under complex vehicle conditions, providing reliable support for the engineering application of vehicle IMU mounting angle.

[0111] like Figure 3 The diagram illustrating the adaptive update process of the onboard IMU for information determination in integrated navigation, as shown below, illustrates the overall process as follows: 1. Initialization and First Weighted Startup After system startup, board initialization is performed first to complete basic parameter and status configuration. Then, the single-set installation angle (a) is calculated, and it is determined whether the system has entered integrated navigation and the new information is small. If the system has not entered integrated navigation or the new information is large, the latest single-set installation angle is used directly. If the system has entered integrated navigation and the new information is small, the system further checks whether three consecutive sets of installation angles are close. If they are close, the weighted result of these three sets is used as the initial installation angle. If they are not close, a new single-set installation angle (b) is calculated, and the subsequent decision-making process begins.

[0112] 2. Dynamic Update and Fusion Weight Determination After calculating the single-group installation angle (b), the system enters the dynamic update phase. First, it determines whether the new group installation angle is close to the current weighted installation angle: if not, the new group installation angle is marked as an outlier, and the current weighted installation angle is used directly without updating; if close, it further determines whether the fusion weight has reached a preset threshold. If the fusion weight reaches the threshold, a fusion update is performed, incorporating the new group installation angle into the weighted calculation and updating the fusion weight; if the threshold is not reached, the current weighted installation angle continues to be used, ensuring a smooth transition of installation angles.

[0113] 3. Outlier and IMU Movement Detection When a new set of installation angles is marked as an outlier, the system enters the anomaly protection branch. First, it checks if the condition of "two consecutive sets of outliers with similar values" is met. If so, it further checks if the integrated navigation information is significant. If the information is significant, it indicates that the IMU has been physically moved, and the original installation angle needs to be replaced with the latest single-set installation angle. If neither condition is met, it returns to recalculating the single-set installation angle to prevent outliers from affecting system stability.

[0114] 4. Reset and Closed-Loop Optimization The entire process supports a board information reset mechanism. If the reset condition is triggered, the system returns to the initialization phase and re-executes the initial installation angle calculation and weighting process. If the reset is not triggered, the dynamic update and anomaly protection logic continues to be executed, forming a complete closed-loop control. Through multiple judgments and adaptive strategies, the estimation accuracy of the installation angle and the robustness of the system under complex vehicle operating conditions are effectively guaranteed.

[0115] The IMU installation angle adaptive fusion system based on integrated navigation information provided by the present invention will be described below. The IMU installation angle adaptive fusion system based on integrated navigation information described below can be referred to in correspondence with the IMU installation angle adaptive fusion method based on integrated navigation information described above.

[0116] Figure 4 This is a schematic diagram of the structure of the IMU installation angle adaptive fusion system based on integrated navigation information provided in an embodiment of the present invention, as shown below. Figure 4 As shown, it includes: a first establishment module 41, a second establishment module 42, a first calculation module 43, and a second calculation module 44, wherein: The first module 41 is used to establish a mathematical model of the vehicle-mounted IMU's multi-coordinate system and attitude transformation, defining the vehicle coordinate system, IMU coordinate system, and NE-GYN navigation coordinate system, and constructing the attitude matrix correlation relationship; the second module 42 is used to establish a static gravity field vector calculation model for the vehicle, utilizing the characteristic that the accelerometer is only sensitive to the gravity vector in a static state, to accurately calculate the IMU roll and pitch installation angles without small angle approximations; the first calculation module 43 is used to obtain the spatial displacement vector through inertial navigation attitude velocity integration recursively, solve the horizontal heading installation angle, and compare it with the average acceleration vector angle method to achieve mutual verification and validation of calibration results; the second calculation module 44 is used to obtain the single effective installation angle estimate based on attitude time-series convergence discrimination and working condition reliability screening, and use the solution reliability index and navigation information to complete the online adaptive iterative correction of the three-axis installation deviation angle.

[0117] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call logic instructions in the memory 530 to execute an IMU installation angle adaptive fusion method based on integrated navigation information. This method includes: establishing a mathematical model of the vehicle-mounted IMU multi-coordinate system and attitude transformation, defining the vehicle coordinate system, IMU coordinate system and NE-G navigation coordinate system, and constructing the attitude matrix correlation; establishing a vehicle static gravity field vector solution model, utilizing the characteristic that the accelerometer is only sensitive to the gravity vector in the static state, and accurately solving the IMU roll installation angle and pitch installation angle under the condition of no small angle approximation; obtaining the spatial displacement vector through inertial navigation attitude velocity integration recursion, solving the horizontal plane heading installation angle, and comparing it with the average acceleration vector angle method to achieve mutual verification of calibration results; relying on attitude time series convergence discrimination and working condition credibility screening, obtaining the single effective installation angle estimate, and using the solution credibility index and navigation information to complete the online adaptive iterative correction of the three-axis installation angle. Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An IMU installation angle adaptive fusion method based on integrated navigation information, characterized in that, include: A mathematical model for vehicle-mounted IMU multi-coordinate system and attitude transformation is established, defining the vehicle coordinate system, IMU coordinate system and NE-G ground navigation coordinate system, and constructing the attitude matrix correlation relationship; A static gravity field vector calculation model for the vehicle is established. Taking advantage of the characteristic that the accelerometer is only sensitive to the gravity vector in a static state, the roll and pitch installation angles of the IMU are accurately calculated without small angle approximations. The spatial displacement vector is obtained by recursively integrating the attitude velocity of the inertial navigation system. The horizontal heading angle is then calculated and compared with the mean acceleration vector angle method to achieve mutual verification and validation of the calibration results. Based on attitude timing convergence discrimination and working condition reliability screening, the estimated effective installation angle for a single operation is obtained. By utilizing the solution reliability index and navigation information, the online adaptive iterative correction of the three-axis installation deflection angle is completed.

2. The IMU installation angle adaptive fusion method based on integrated navigation information according to claim 1, characterized in that, A mathematical model for the multi-coordinate system and attitude transformation of the vehicle-mounted IMU is established, defining the vehicle coordinate system, the IMU coordinate system, and the NE-G coordinate system. The relationship between the attitude matrices is constructed, including: The attitude matrix of the IMU relative to the navigation system is obtained by simultaneously combining the attitude matrix of the vehicle body relative to the navigation system and the attitude matrix of the IMU relative to the vehicle body mounting. In the formula, The attitude matrix of the IMU relative to the navigation frame. This is the vehicle's attitude matrix relative to the navigation system. This is the mounting attitude matrix of the IMU relative to the vehicle body.

3. The IMU installation angle adaptive fusion method based on integrated navigation information according to claim 1, characterized in that, A static gravity field vector solution model for the vehicle is established, utilizing the characteristic that the accelerometer is only sensitive to the gravity vector in a static state, including: The vehicle is positioned horizontally, without slope or vibration interference, and the vehicle coordinate system is completely aligned with the local horizontal navigation coordinate system. The expression for the gravity vector in the navigation coordinate system is: Under static conditions, the IMU accelerometer outputs a specific force vector. Ignoring sensor bias and random noise, the coordinate transformation relationship is satisfied: 。 4. The IMU installation angle adaptive fusion method based on integrated navigation information according to claim 3, characterized in that, Accurate calculation of IMU roll and pitch installation angles without small-angle approximations, including: The approximate strict global attitude matrix with no small angles corresponding to the IMU's roll and pitch mounting attitudes relative to the vehicle body: Normalize the static comparison data: By combining the geometric relationships of gravity vector projection and rigorous attitude matrix derivation, we obtain accurate formulas for calculating the roll and pitch installation angles without angular constraints: 。 5. The IMU installation angle adaptive fusion method based on integrated navigation information according to claim 1, characterized in that, The spatial displacement vector is obtained by recursively integrating the attitude velocity of the inertial navigation system, and the horizontal heading angle is solved, including: The inertial navigation attitude is recursively derived through quaternion attitude update. The attitude update differential equation in the navigation coordinate system is: In the formula, The angular velocity vector of the relative navigation frame observed by the IMU. This refers to quaternion multiplication operations; The attitude matrix of the IMU relative to the navigation frame can be obtained by updating the quaternion in real time. ; The inertial navigation velocity update formula under the navigation system is: Integrating the velocity in the time domain yields the IMU's displacement vector in the navigation frame: Transform the navigation system displacement vector to the IMU carrier coordinate system: When the vehicle is traveling in a straight line, its actual trajectory is strictly along the positive X-axis. Therefore, the ideal displacement vector of the vehicle has only a longitudinal component, and the lateral displacement is zero. Due to the existence of the heading installation angle The displacement vector calculated by the IMU has a fixed horizontal angle relative to the ideal displacement vector of the vehicle body. When only the heading deviation is retained, the rotation transformation matrix between the IMU and the vehicle body on the horizontal plane is: Displacement vectors satisfy coordinate transformation relationships Expanding the two-dimensional components of the horizontal plane yields: Eliminate displacement amplitude The principal value of the heading installation angle, obtained by resolving the inertial navigation displacement vector, is obtained without any approximations: 。 6. The IMU installation angle adaptive fusion method based on integrated navigation information according to claim 5, characterized in that, By combining the average acceleration vector angle method for comparison, the calibration results are mutually verified and validated, including: Under the condition of smooth, straight-line driving, the vehicle body has no continuous lateral motion, and the average acceleration of the vehicle body is strictly along the X-axis direction of travel. The ideal average acceleration vector of the vehicle body is: Multiple frames of IMU acceleration data were collected during dynamic driving, and the average acceleration vector in the IMU coordinate system was obtained after mean filtering. Roll and pitch deviations have been fully compensated, and the acceleration vector is only affected by the horizontal heading angle. The coordinate transformation satisfies: Expanding the horizontal plane constraints, and considering the characteristic that the lateral average acceleration is 0 in the straight-line driving condition. The constraint equations are obtained as follows: The heading installation angle verification value obtained by the acceleration vector matching method is obtained by solving: The consistency of the results was verified and calibrated using the inertial navigation recursive method and the average acceleration vector matching method, respectively.

7. The IMU installation angle adaptive fusion method based on integrated navigation information according to claim 1, characterized in that, The convergence determination of the heading and installation angle sequence and the confidence verification of the installation angle include: Get the current epoch Including the recent Each epoch, constructing the heading installation angle sequence. Set the heading angle deviation threshold between adjacent epochs. A sequence is considered convergent if it simultaneously satisfies the following constraints: Set horizontal attitude perturbation threshold Heading and turning disturbance threshold Let the epoch interval of the convergent sequence be... The quantification constraints are: In the formula, For the epoch interval corresponding to the convergence sequence of the heading installation angle; for The sum of variances of the roll and pitch angle sequences within the interval quantifies the overall perturbation of the horizontal attitude. for The total heading angle within the interval reflects whether the vehicle is maintaining straight-line travel; The variance calculation operator is expressed as follows: in, For the interval epoch number, For sequence The mean; , Horizontal reference coordinate system Next The roll and pitch angles of the epoch. For the first in this coordinate system Epoch Gyroscope Effective output value of axis The epoch sampling interval; Construct an installation angle confidence model to assess the reliability of the solution: In the formula, For the installation angle confidence, , These are positive weighting coefficients, adjusted according to the degree of influence of the operating condition disturbance; The larger the value, the higher the reliability of the solution; set a confidence threshold. , The solution is deemed invalid and the result is discarded. It is determined to be valid and retained.

8. The IMU installation angle adaptive fusion method based on integrated navigation information according to claim 7, characterized in that, The final value calculation of the heading installation angle is integrated with the three-axis installation angle, including: After the heading installation angle sequence converges and the confidence check passes, The calculated heading and installation angle values ​​for each epoch are filtered using a sliding window mean filter, and then... The mean of each epoch is used as the optimal estimate of the heading angle: Roll installation angle calculated using the gravity projection method Pitch installation angle With respect to the above-mentioned optimal heading installation angle By integrating the data, we obtain the three-axis mounting angle vectors of the IMU relative to the vehicle coordinate system: 。 9. The IMU installation angle adaptive fusion method based on integrated navigation information according to claim 8, characterized in that, Adaptive updates for integrated navigation information determination include: Definition of the first The effective solution result is: ,right First, a validity screening of the operating conditions is performed to ensure that the calculation results correspond to normal driving conditions. The specific judgment criteria are as follows: Preliminary Position Information Assessment: Calculation of Position Information Module Length for GNSS-INS Integrated Navigation ,like ( If the location information threshold is used, then the solution condition is determined to be without obvious abnormalities. Stability determination of consecutive solutions: To verify the consistency of three consecutive valid solutions, the following conditions must be met simultaneously. and , To set an installation angle deviation threshold, ensuring that the calculation results are free from sudden fluctuations; When both of the above conditions are met, the determination is made. , , For fusionable results, define For the first If there are three fusionable results, then these three fusionable results are denoted as... , , Only such fusion-compatible results are retained for subsequent weighted fusion; Using the confidence level of each set of mounting angles as the weight, the weighted fusion of multiple sets of fusionable mounting angles is completed in two steps: Initial fusion: After accumulating 3 sets of fusionable results, initial fusion is triggered to build a stable baseline. The fusion formula is as follows: In the formula, This represents the total number of fusionable results; for The installation angle is obtained by weighted fusion of the fusion results; This is used as the cumulative confidence weight; this step effectively avoids random disturbances in single-batch calculations by redundantly fusing multiple sets of data. Dynamic iterative fusion: After the initial fusion is completed, each additional set of valid solution results selected by the operating conditions is further fused. ,determination If this condition is met, lightweight iterative fusion will be initiated, eliminating the need to store historical data. The iterative formula is as follows: In the formula, For the first The mounting angle after secondary fusion; For the front The cumulative confidence weight of the sub-fusion This is the updated cumulative weight; Outlier detection and adaptive updates are performed on the newly calculated installation angle group to eliminate abnormal operating condition deviations: Outlier detection: If the newly added valid solution does not meet the requirements... If it is, then it is judged as an outlier. They will not be included in the fusion process for the time being to avoid affecting the overall estimation accuracy due to a single abnormal operating condition. IMU relocation determination: A physical relocation of the IMU is determined to have occurred if and only if both of the following conditions are met simultaneously: Timing anomaly consistency: Two consecutive sets of solution results are outliers, and satisfy the following conditions: This indicates that the installation angle variation is consistent, thus ruling out random anomalies; Location-based new information evidence: If This indicates that the GNSS-INS integrated navigation position information is outside the normal range, which can further verify that the anomaly was caused by IMU movement; When both conditions are met, the fusion benchmark is updated to the latest valid solution; if only one condition is met, it is judged as a normal anomaly, the abnormal data is removed and the current fusion result remains unchanged. Adaptive stable update: Set a stable threshold for fusion weights If the cumulative confidence weights satisfy If the installation angle is determined to be stable, dynamic updates are stopped and the current fusion value is used; if the condition is not met, a valid new solution group is included in the fusion, and the weighted fusion value is dynamically updated. When the fusion weight Reaching the preset threshold When the fusion result has fully utilized the redundancy of multiple batches of data and the installation angle estimation tends to stabilize, the iterative update is stopped, and the current weighted fusion value is taken as the final optimal estimate of the IMU's three-axis installation angle. .

10. An IMU installation angle adaptive fusion system based on integrated navigation information, characterized in that, include: The first module is used to establish a mathematical model of the vehicle-mounted IMU's multi-coordinate system and attitude transformation, define the vehicle coordinate system, IMU coordinate system and the NE-G coordinate system, and construct the attitude matrix relationship. The second module is used to establish a static gravity field vector calculation model for the vehicle. It utilizes the characteristic that the accelerometer is only sensitive to the gravity vector in a static state to accurately calculate the IMU roll and pitch angles without small angle approximations. The first calculation module is used to recursively obtain the spatial displacement vector through the integral of the inertial navigation attitude velocity, solve the horizontal heading installation angle, and compare it with the average acceleration vector angle method to realize mutual verification and validation of the calibration results. The second calculation module is used to obtain the estimated value of the single effective installation angle based on attitude timing convergence discrimination and working condition confidence screening. It then uses the solution confidence index and navigation information to complete the online adaptive iterative correction of the three-axis installation deflection angle.

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