Optoelectronic pod multi-source IMU cooperative calibration ground positioning method

Abstract

The present application relates to unmanned aerial vehicle aerial reconnaissance and target positioning technical field, especially to a kind of photoelectric pod multi-source IMU cooperative calibration ground positioning method. Including: real-time synchronous acquisition main IMU and the angular velocity and acceleration of turret IMU, Beidou positioning data and photoelectric turret encoder angle data, define three coordinate systems and output time synchronization data stream;Subsequently, through cross-correlation analysis, the angular velocity data of double IMU is extracted, and the initial Euler angle deviation is obtained by analysis, to complete alignment;Based on the initial deviation, initialize enhanced EKF, for large lever arm effect and flexible deformation, carry out online cooperative calibration;Fusion rotation matrix and encoder angle, solve the unit vector of navigation system sight line;Rod arm deformation compensation is carried out to Beidou data, combined with laser ranging value, solve target WGS-84 coordinate;Finally, carry out anti-interference filtering, IMU random error calibration and pitch angle inclination compensation, and feedback correction previous step.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) aerial reconnaissance and target localization technology, and in particular to a ground localization method using a multi-source IMU collaborative calibration of an optoelectronic pod. Background Technology

[0002] The flight conditions of unmanned helicopters are characterized by significant strong vibrations and large stick effects. Combined with the inherent technical defects of traditional electro-optical pod positioning methods, existing technologies struggle to overcome positioning accuracy bottlenecks. Problems such as dynamic error accumulation, insufficient observation capabilities, and weak anti-interference are compounded, severely restricting the high-precision application of electro-optical pods on unmanned helicopter platforms. The specific technical pain points are reflected in the following two aspects:

[0003] On the one hand, traditional optoelectronic pod-based ground target positioning methods have inherent technical defects and cannot meet the needs of high-precision positioning. (1) Error correction is performed by ground static calibration. This method can only calibrate the fixed error when the equipment is stationary. It cannot correct the dynamic deviations caused by temperature changes, rotor vibration, and installation angle drift during the flight of the UAV. As a result, the error of the Beidou antenna arm, the installation angle deviation of the main inertial navigation system, and the deviation of the turret rotation center continue to accumulate. The accumulated error can reach the decimeter level, which directly affects the positioning accuracy. (2) The positioning system relies only on the main inertial measurement unit (IMU) of the UAV to complete motion perception. The main IMU is difficult to effectively perceive the nonlinear motion of the photoelectric turret rotation center. As a result, the line-of-sight pointing error increases exponentially with the increase of the turret pitch angle. The larger the pitch angle, the more obvious the positioning deviation. (3) The system is highly sensitive to interference from complex flight environments. Electromagnetic interference, multipath effects, etc. can easily cause the Beidou positioning signal to jump. The traditional extended Kalman filter (EKF) method fails to filter under such circumstances and cannot stably output reliable positioning data, which further reduces the accuracy and stability of positioning.

[0004] On the other hand, the special flight conditions of unmanned helicopters bring technical challenges, and traditional compensation and positioning methods are completely ineffective. (1) The large arm effect is significant. The installation positions of the Beidou antenna and the main IMU of the unmanned helicopter are far apart, and the arm length can reach several meters. During the helicopter's maneuvering flight, the traditional linear velocity compensation method is difficult to adapt to the motion characteristics of the large arm. The compensation error can reach the meter level, which cannot meet the core requirements of meter-level positioning. (2) Strong vibration causes flexible deformation of the fuselage and arm. The continuous strong vibration generated by the rotor rotation of the unmanned helicopter will cause elastic deformation of the fuselage and arm, so that the arm vector is not rigidly fixed. Traditional methods are all based on the rigid arm model for error compensation. This compensation logic is completely ineffective in the flexible deformation scenario and cannot effectively correct the error caused by deformation. (3) Multiple errors form a coupling superposition effect. The arm deformation error, IMU installation error, and turret offset error are coupled in multiple ways. A single error compensation method can only correct a certain type of error and cannot solve the problem of continuous deterioration of positioning accuracy caused by multiple error coupling, which further increases the difficulty of achieving high-precision positioning.

[0005] In summary, existing technologies cannot solve their own limitations in static calibration, insufficient single IMU observation capabilities, and weak anti-interference capabilities. Furthermore, they are ill-suited to the special operating conditions of unmanned helicopters, such as strong vibrations and large-arm effects, resulting in the electro-optical pod's ground target positioning accuracy consistently failing to reach meter-level requirements. Therefore, there is an urgent need to develop an electro-optical pod positioning method suitable for strong vibrations and large-arm effects, enabling real-time online calibration and compensation of various dynamic errors during flight, while simultaneously improving the system's vibration and anti-interference capabilities to meet the high-precision positioning requirements of various scenarios. Summary of the Invention

[0006] To address the aforementioned problems, this invention provides a method for ground positioning using a multi-source IMU collaborative calibration method for an optoelectronic pod.

[0007] The purpose of this invention is to provide a method for ground positioning using a multi-source IMU collaborative calibration method for an optoelectronic pod, which specifically includes the following steps:

[0008] S1. Real-time synchronous acquisition of angular velocity and acceleration of the main IMU, angular velocity and acceleration of the turret IMU, navigation system position data output by the Beidou positioning module, and azimuth and pitch angles output by the photoelectric turret encoder; definition of navigation system, machine system, and turret system; output time-synchronized multi-source raw data streams and coordinate system transformation relationships.

[0009] S2. Extract the angular velocities of the main IMU and the turret IMU, solve for the peak position of the cross-correlation function using the cross-correlation analysis method, and analyze the initial Euler angle deviation between the main IMU and the turret system to complete the initial alignment of the two IMUs;

[0010] S3. Initialize the state variables of the enhanced extended Kalman filter with the initial Euler angle deviation, and perform online collaborative calibration for the large lever effect and flexible deformation;

[0011] S4. Use the rotation matrix from the turret system to the machine system to convert the turret IMU data to the machine system, and fuse the attitude matrix from the machine system to the navigation system, the rotation matrix from the turret system to the machine system, and the azimuth and pitch angles output by the photoelectric turret encoder to calculate the line-of-sight unit vector in the navigation system.

[0012] S5. Perform lever deformation compensation on the navigation system position data to obtain the compensated accurate UAV navigation system position; obtain the distance value from the UAV to the target output by the laser rangefinder, and perform vector superposition of the compensated UAV position and the result of multiplying the line-of-sight unit vector by the distance value to calculate the WGS-84 geographic coordinates of the target.

[0013] S6. Based on geographic coordinates and multi-source raw data streams, anti-interference filtering, IMU random error calibration, and turret pitch and tilt angle compensation are performed to provide correction feedback and improve the positioning accuracy of ground targets.

[0014] Preferably, in step S1, the navigation system adopts the Northeast-Northeast Geographic Coordinate System, with the origin being the UAV takeoff point; the machine system origin is the measurement center of the main IMU, with the X-axis pointing towards the UAV's nose; and the turret system origin is the intersection of the azimuth axis and the pitch axis of the photoelectric turret.

[0015] Preferably, in step S2, the cross-correlation function as follows:

[0016] ;

[0017] In the formula, This indicates the time delay, reflecting the phase difference between the two IMU signals; This represents the angular velocity measurement value of the main IMU at time t; Indicates time Angular velocity measurement value of the turret IMU; The initial Euler angle deviation from the turret system to the main system is represented by... The peak position was determined by analysis.

[0018] Preferably, step S3 specifically includes: constructing 25-dimensional state variables including navigation system position error, velocity error, attitude angle error, accelerometer bias, gyroscope bias, BeiDou-main IMU lever arm deformation vector, lever arm vibration mode angle, main IMU-turret rotation center lever arm, and turret installation angle error; combining strong vibration characteristics with vibration spectral density modeling process noise covariance matrix to construct EKF state equations that conform to strong vibration and large lever arm working conditions; substituting multi-source data to complete online real-time estimation of 25-dimensional state variables, solving for and compensating for lever arm elastic deformation and various installation angle errors, and outputting the compensated UAV pose and the attitude matrix from the UAV system to the navigation system.

[0019] Preferably, 25-dimensional state variables X It is expressed as follows:

[0020] ;

[0021] in, Indicates the position error of the navigation system; Indicates the speed error of the navigation system; Indicates the attitude angle error of the navigation system; This indicates that the accelerometer has zero bias. This indicates that the gyroscope has zero bias. This represents the deformation vector of the BeiDou-main IMU arm. , , , These represent the deformation of the lever arm along the x, y, and z axes, respectively. Indicates the modal angle of the lever arm vibration; Indicates the main IMU-turret rotation center arm; This indicates the turret installation angle error.

[0022] Preferably, the extended Kalman filter state equation is:

[0023] ;

[0024] In the formula, State variables X The rate of change over time; Represents a nonlinear state transition function; The process noise vector is represented by a Gaussian distribution. N ; This represents the process noise covariance matrix, which includes the vibration spectral density parameter.

[0025] Preferably, in step S4, the formula for calculating the unit vector of the view axis is:

[0026] ;

[0027] In the formula, Represents the line-of-sight unit vector in the navigation system; The attitude matrix representing the machine system to the navigation system; Represents the rotation matrix from the turret system to the machine system; and These represent the azimuth and pitch angles output by the photoelectric turret encoder, respectively.

[0028] Preferably, in step S5, the lever arm deformation compensation formula is:

[0029] ;

[0030] In the formula, Indicates the location of the drone's navigation system; For navigation system location data; The attitude matrix representing the machine system to the navigation system; Indicates the nominal lever arm vector of the BeiDou-main IMU; This represents the deformation vector of the BeiDou-main IMU arm;

[0031] WGS-84 geographic coordinates of the target The solution formula is as follows:

[0032] ;

[0033] In the formula, Represents the line-of-sight unit vector in the navigation system; This indicates the distance value from the drone to the target, output by the laser rangefinder.

[0034] Preferably, step S6 includes the following sub-steps:

[0035] S61. Anti-interference multi-source fusion filtering: Extract the observation innovation vector at time k in the filtering process, define the innovation adaptive factor, construct an adaptive Kalman filter model and incorporate the factor, complete the full data filtering optimization, and obtain accurate data after eliminating interference;

[0036] S62. Online calibration of random errors of dual IMUs: Based on the turret IMU data stream output by S61, the Allan variance analysis method is used to extract angular velocity measurement data, calculate the Allan standard deviation and separate various noise parameters, estimate the noise parameters through curve fitting, and perform zero-bias drift suppression and correction on the dual IMU measurement data.

[0037] S63. Tilt Compensation Turret Pointing: Based on the zero-bias instability parameters output by S62, the gravity vector under the turret system is obtained by measuring with the turret IMU, the unit vector of the turret system Z-axis is extracted, and the pitch angle compensation is obtained by dot product, ratio and arctangent calculation. The accurate pitch angle is obtained by adding it to the original pitch angle and feeding it back to S4 to recalculate the line-of-sight pointing vector.

[0038] Preferably, in step S61, the formula for solving the Kalman gain matrix is:

[0039] ;

[0040] In the formula, Represents the Kalman gain matrix; Represents the state prediction covariance matrix; Represents the observation matrix; Represents the observation noise covariance matrix; Indicates the information adaptive factor. , This represents the observation information vector at time k;

[0041] In step S62, the Allan standard deviation formula is:

[0042] ;

[0043] In the formula, Indicates the Allan standard deviation; Indicates the relevant time; Indicates the quantization noise figure; This indicates a random walk at an angle; This indicates zero-bias instability; This indicates a rate random walk; Indicates a rate ramp;

[0044] In step S63, the compensated pitch angle The calculation formula is as follows:

[0045] ;

[0046] In the formula, Indicates the compensated pitch angle; This represents the gravity vector (m / s) measured by the turret IMU. 2 ), , These represent the components of the gravity vector along the x, y, and z coordinate axes, respectively. This represents the unit vector along the Z-axis of the turret system.

[0047] Compared with the prior art, the present invention can achieve the following beneficial effects:

[0048] By adding a turret IMU to directly sense the nonlinear motion of the rotation center of the photoelectric turret, a dual IMU collaborative calibration system is constructed, which improves the observability of installation error from 6 dimensions to 25 dimensions, completely solving the problem of insufficient observation capability of traditional single IMU. When the turret is pitched at 45°, the line-of-sight pointing error is reduced to less than 0.5m, which significantly improves the positioning deviation at large pitch angles.

[0049] The enhanced EKF state variables include the Beidou-main IMU lever deformation vector and lever vibration mode angle, which specifically model the time-varying deformation of the lever caused by strong vibration, replacing the traditional rigid lever compensation logic, reducing the decimeter-level cumulative error of the lever to the centimeter level, and adapting to the maneuvering flight characteristics of the multi-meter-level large lever of unmanned helicopters.

[0050] By suppressing the 20~2000Hz wideband vibration noise through cross-correlation analysis, combining the new information adaptive EKF to resist BeiDou positioning jump interference, and using Allan variance calibration to suppress IMU zero bias drift, and then correcting the turret pointing error through gravity vector tilt compensation, the multi-dimensional collaboration reduces the error fluctuation during BeiDou jump by 70%, and the vibration resistance is 10 times better than the traditional method.

[0051] Real-time online estimation and compensation of three dynamic errors: boom deformation, installation angle drift, and turret offset; continuously correcting the line-of-sight pointing and target coordinate calculation results.

[0052] It can be upgraded without major modifications to the existing optoelectronic pod hardware, simply by adding a turret IMU. It is compatible with various unmanned helicopter platforms and can be widely used in a variety of scenarios with stringent requirements for positioning accuracy and stability. Attached Figure Description

[0053] Figure 1 This is a flowchart of a ground positioning method for multi-source IMU collaborative calibration provided by an embodiment of the present invention. Detailed Implementation

[0054] In the following description, embodiments of the invention will be described with reference to the accompanying drawings. In the description below, the same modules are denoted by the same reference numerals. Where the same reference numerals are used, their names and functions are also the same. Therefore, their detailed description will not be repeated.

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not constitute a limitation thereof.

[0056] This invention provides a multi-source IMU collaborative calibration method for ground positioning using an optoelectronic pod. It is applicable to ground target positioning using multi-source IMU collaborative calibration of optoelectronic pods subjected to strong vibrations and large boom effects. A turret-IMU is added to the optoelectronic pod, working in conjunction with the UAV's main IMU to construct a technical system for dual-IMU collaborative calibration, dynamic deformation modeling, and multi-source anti-interference filtering. This system sequentially completes multi-sensor data acquisition and coordinate system definition, vibration-resistant initial alignment and cross-correlation verification, online collaborative calibration of the large boom and flexible deformation, high-precision line-of-sight pointing vector calculation, target geographic coordinate calculation, anti-interference multi-source fusion filtering, online calibration of dual IMU random errors, and tilt compensation for turret pointing. Simultaneously, a dynamic calibration closed loop is constructed to achieve online estimation and compensation of the three major dynamic errors, ultimately outputting target geographic coordinates with meter-level accuracy. See also Figure 1 Specifically, it includes the following steps:

[0057] S1. Multi-sensor data acquisition and coordinate system definition: Real-time acquisition of multi-source sensor data, definition of coordinate systems, and output of time-synchronized multi-source raw data streams and coordinate system transformation relationships; specifically including:

[0058] Real-time acquisition of multi-source sensor data: Simultaneously acquiring four types of core data during the UAV's flight process, namely the three-dimensional angular velocity vector of the main IMU in the body coordinate system. and acceleration vector Angular velocity of the turret IMU in the turret coordinate system and acceleration Location data under the navigation system output by the Beidou positioning module (longitude ,latitude ,altitude ), the angle (azimuth angle) output by the photoelectric turret encoder and pitch angle );

[0059] Define three types of core coordinate systems:

[0060] Navigation system ( n The system adopts the Northeastern Universe (ENU) geographic coordinate system, with the origin of the coordinate system being the takeoff point of the UAV, providing a unified geospatial reference for all positioning data.

[0061] Machine system ( b The origin of the coordinate system is the measurement center of the main IMU, and the X-axis points towards the nose of the UAV, adapting to the measurement data space of the main IMU.

[0062] Rotary tower system ( t The origin of the coordinate system is the intersection of the azimuth and pitch axes of the photoelectric turret, which is compatible with the measurement data space of the Turret-IMU and the photoelectric turret encoder.

[0063] The purpose of this step is to establish a unified spatiotemporal reference, solve the challenge of data fusion from multiple heterogeneous sensors, and lay the spatial and temporal foundation for all subsequent error calibration and calculation steps. It outputs time-synchronized multi-source raw data streams and coordinate system transformation relationships, providing a data basis for the initial vibration-resistant alignment and cross-correlation verification of S2.

[0064] S2. Vibration-resistant initial alignment and cross-correlation verification: Extracting the angular velocity vector of the main IMU. and the angular velocity of the turret IMU As the data source for analysis, cross-correlation analysis is used to suppress the interference of high-frequency vibration noise on IMU data, achieving robust initial alignment between the main IMU and the turret IMU; firstly, a cross-correlation function is constructed. The peak position is determined, and the initial Euler angle deviation between the mechanical system and the turret system is analytically derived from the peak characteristics to complete the initial alignment operation of the dual IMUs; cross-correlation function as follows:

[0065] ;

[0066] In the formula, It indicates the delay amount (in seconds), reflecting the phase difference between the two IMU signals; This represents the angular velocity measurement value of the main IMU at time t; Indicates time Angular velocity measurement value of the turret IMU; The initial Euler angle deviation (in radians) from the turret system to the engine system is represented by... The peak position was determined by analysis.

[0067] The purpose of this step is to suppress high-frequency noise interference caused by strong vibrations, achieve precise initial alignment between the main IMU and the turret IMU, and provide reliable initial attitude parameters for subsequent error calibration. Output initial Euler angle deviation. It is used as the initialization parameter of the extended Kalman filter (EKF) in the online collaborative calibration of the S3 large arm and flexible deformation, and the error of the parameter is guaranteed to be <0.1°.

[0068] S3. Online Co-calibration of Large Arm and Flexible Deformation: The initial Euler angle deviation output by S2 is used to initialize the state variables of the Enhanced Extended Kalman Filter (EKF), and online co-calibration is carried out for the large arm effect and flexible deformation.

[0069] Specifically, first, a 25-dimensional state variable is constructed. X :

[0070] ;

[0071] in, Represents the navigation system position error (meters), a three-dimensional vector (longitude / latitude / altitude error), occupying 3 dimensions; Represents the navigation system velocity error (m / s), a three-dimensional vector (x / y / z-axis velocity error), occupying 3 dimensions; Represents the navigation system attitude angle error (radians), a three-dimensional vector (roll / pitch / yaw angle error), occupying 3 dimensions; Indicates zero bias of the accelerometer (m / s) 2 ), a three-dimensional vector (x / y / z biased towards zero), occupying 3 dimensions; This indicates zero bias of the gyroscope (radians / second), a three-dimensional vector (zero bias in the x / y / z directions), occupying 3 dimensions; This represents the deformation vector (meters) of the BeiDou-main IMU arm, a three-dimensional vector. , , , These represent the deformation of the lever arm along the x, y, and z axes, respectively. Represents the modal angle of the lever arm vibration (in radians), a scalar; Represents the main IMU-turret rotation center arm (meters), a three-dimensional vector; Represents the turret installation angle error (in radians), a three-dimensional vector;

[0072] Based on the strong vibration characteristics of UAV flight, and by using the noise covariance matrix Q in the vibration spectral density modeling process, an EKF state equation that conforms to the strong vibration and large boom conditions is constructed:

[0073] ;

[0074] In the formula, State variables X The rate of change over time; Represents a nonlinear state transition function; The process noise vector is represented by a Gaussian distribution. N ); The process noise covariance matrix is ​​represented, which includes the vibration spectral density parameter.

[0075] Substituting the multi-source data collected in step S1 and the initialization parameters in step S2 into the enhanced EKF model, online real-time estimation of 25-dimensional state variables is completed. The values ​​of the elastic deformation of the boom arm and various installation angle errors are accurately calculated. Then, based on the calculation results, error compensation is performed on the UAV's pose, and the compensated UAV pose is output. Attitude matrix from the machine system to the navigation system ;

[0076] The purpose of this step is to estimate and compensate for the elastic deformation of the boom arm and various installation angle errors online, solving the problem of error accumulation caused by the large boom arm effect and flexible deformation. The compensated UAV pose is then output. Attitude matrix from the machine system to the navigation system This provides the core pose and attitude matrix parameters for S4 high-precision line-of-sight pointing vector calculation.

[0077] S4. High-precision line-of-sight pointing vector calculation: using the rotation matrix from the turret system to the machine system. Convert turret IMU data to the system architecture to unify the spatial coordinates of the turret and system architectures; fuse the attitude matrix from the system architecture to the navigation system. Rotation matrix of turret system to machine system and the azimuth angle output by the photoelectric turret encoder and pitch angle Substituting the azimuth and elevation angles into the angle matrix calculated from the line-of-sight pointing vector, and then performing matrix multiplication, the unit line-of-sight vector in the navigation system is calculated. This completes the high-precision calculation of the line-of-sight pointing; the solution formula is as follows:

[0078] ;

[0079] The purpose of this step is to fuse the attitude and encoder angle data from the dual IMUs to calculate a vibration-resistant line-of-sight pointing vector, thus avoiding the impact of turret motion coupling errors on line-of-sight pointing accuracy. This outputs the line-of-sight unit vector in the navigation system. , which serves as the core input parameter for calculating the geographic coordinates of the S5 target.

[0080] S5. Target geographic coordinate calculation: Calculate the position data under the navigation system output by the BeiDou positioning module. Perform lever arm deformation compensation, and adjust the nominal lever arm vector of the Beidou-main IMU. With Beidou-Main IMU boom arm deformation vector Summation, then attitude matrix from machine system to navigation system. After completing the spatial transformation, from the original BeiDou location After deducting the deformation value, the compensated precise position of the UAV navigation system is obtained. :

[0081] ;

[0082] Then obtain the distance value from the drone to the target output by the laser rangefinder. The compensated drone position With the unit vector of the eye axis Multiply by distance value The results are vector-overlaid to calculate the target's WGS-84 geographic coordinates. (Includes longitude, latitude, and altitude information):

[0083] ;

[0084] The purpose of this step is to fuse the lever arm compensation position with the line-of-sight pointing vector to complete the preliminary calculation of the target geographic coordinates, achieving the basic data output for meter-level positioning. Output the preliminary calculated target geographic coordinates. It is then transmitted to S6 for anti-interference multi-source fusion filtering.

[0085] S6. Parallel Enhancement Processing (Anti-interference, Error Suppression, Tilt Compensation): Based on the target geographic coordinates output by S5 and multi-source raw data streams, three types of parallel enhancement processing are simultaneously performed: anti-interference filtering, IMU random error calibration, and turret pitch and tilt compensation. These processes improve positioning accuracy from three dimensions: target coordinate optimization, sensor data correction, and line-of-sight pointing precision. These three processes are parallel sub-steps with interconnected data sources, ultimately providing corrective feedback to the preceding steps and improving the overall accuracy and stability of ground target positioning. Specifically, this includes:

[0086] S61. Anti-interference multi-source fusion filtering: To address the BeiDou positioning jump problem caused by electromagnetic interference and multipath effects, anti-interference multi-source fusion filtering is performed on the preliminary target geographic coordinates and turret IMU measurement data output by S5.

[0087] Specifically, the observation innovation vector at time k in the filtering process is extracted. This vector represents the deviation between the observed and predicted values, and the innovation adaptive factor is defined accordingly. This enables dynamic adjustment of the observation noise covariance; subsequently, an adaptive Kalman filter model is constructed, incorporating the innovation adaptive factor. Incorporating Kalman gain matrix The solution process is introduced into the solution formula. ( (As the identity matrix), when BeiDou positioning experiences signal jumps or abnormal observation data, the new information adaptive factor... This will increase synchronously, thereby increasing the observation noise covariance, effectively reducing the interference of outlier observations on the filtering results; finally, the target geographic coordinates initially calculated by S5 will be... The measurement data from the turret IMU is substituted into the adaptive Kalman filter model to complete the filtering optimization calculation of the full data, obtaining accurate data after eliminating interference; the calculation formula is as follows:

[0088] ;

[0089] In the formula, Represents the Kalman gain matrix; Represents the state prediction covariance matrix; Represents the observation matrix; Represents the observation noise covariance matrix; Indicates the information adaptive factor. , This represents the observation information vector at time k (the deviation between the predicted and measured values).

[0090] The purpose of this step is to effectively suppress environmental interference caused by BeiDou positioning jumps and multipath effects, optimize the accuracy of target geographic coordinates and turret IMU measurement data, and avoid abnormal interference data affecting subsequent error calibration results. The output is the filtered and optimized target geographic coordinates and turret IMU data stream, where the turret IMU data stream provides a reliable and interference-free data source for online random error calibration of the S62 dual IMUs.

[0091] S62. Online calibration of random errors of dual IMUs: Based on the turret IMU data stream output by S6, the Allan variance analysis method is used to carry out online calibration of random errors of dual IMUs, with a focus on suppressing the IMU zero bias drift problem;

[0092] Specifically, angular velocity measurement data is extracted from the turret IMU data stream, and relevant time is introduced according to the Allan variance calculation formula. The Allan standard deviation is calculated. The quantization noise figure is separated from the solution of the Allan standard deviation. random walk at angles Zero bias instability Rate random walk Rate ramp The noise parameters of the turret IMU are as follows:

[0093] ;

[0094] In the formula, Indicates the Allan standard deviation; Indicates the relevant time (seconds); Indicates the quantization noise figure; Indicates the angle random walk (rad / √s); This indicates zero bias instability (rad·s / √Hz). This represents a rate random walk (rad·s) 3 / 2 / √Hz); Indicates the rate ramp (rad / s) 2 );

[0095] By fitting Allan variance curves, various noise parameters are estimated in real time and accurately, with a focus on extracting the zero-bias instability parameters. Then, based on the estimated full noise parameters, the measurement data of the turret IMU are subjected to zero-bias drift suppression and correction. At the same time, the noise parameters are used as a reference for the correction of the random error of the main IMU, and the online calibration of the random error of the dual IMU is completed.

[0096] The purpose of this step is to estimate IMU noise parameters in real time, effectively suppress IMU zero-bias drift, and improve the accuracy and reliability of dual IMU measurement data. It also outputs the zero-bias instability parameters of the turret IMU. This provides the core basis for gravity tilt angle compensation for the orientation of the S63 tilt-compensating turret.

[0097] S63. Based on zero-bias instability parameters To address the non-orthogonal error between the turret system and the gravity vector, tilt angle compensation and precise correction of the turret pitch angle are carried out to improve the pointing accuracy of the photoelectric turret.

[0098] Specifically, the gravity vector under the turret system is obtained by measuring with the turret IMU. Simultaneously extract the Z-axis unit vector of the turret system. Perform a dot product operation, and then combine the result with the gravity vector. The ratio of the module length is calculated, and the arctangent of this ratio is performed to obtain the tilt angle compensation amount. This compensation amount is then compared with the original tilt angle output by the photoelectric turret encoder. Add them together to calculate the compensated pitch angle. The calculation formula is as follows:

[0099] ;

[0100] In the formula, Indicates the compensated pitch angle (in radians); This represents the gravity vector (m / s) measured by the turret IMU. 2 ), , These represent the components of the gravity vector along the x, y, and z coordinate axes, respectively. Represents the unit vector along the Z-axis of the turret system;

[0101] The purpose of this step is to compensate for the non-orthogonality error between the turret system and the gravity vector, accurately correct the elevation angle of the photoelectric turret, eliminate the influence of tilt deviation on the line-of-sight pointing, and further improve the accuracy of the line-of-sight pointing calculation. The compensated, precise elevation angle is output and fed back to the S4 high-precision line-of-sight pointing vector calculation step, replacing the original elevation angle in the recalculation of the line-of-sight pointing vector, forming a dynamic calibration closed loop, and continuously optimizing the target positioning accuracy.

[0102] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0103] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for ground positioning using multi-source IMU collaborative calibration of an optoelectronic pod, characterized in that: Specifically, the steps include the following: S1. Real-time synchronous acquisition of angular velocity and acceleration of the main IMU, angular velocity and acceleration of the turret IMU, navigation system position data output by the Beidou positioning module, and azimuth and pitch angles output by the photoelectric turret encoder; definition of navigation system, machine system, and turret system; output time-synchronized multi-source raw data streams and coordinate system transformation relationships. S2. Extract the angular velocities of the main IMU and the turret IMU, solve for the peak position of the cross-correlation function using the cross-correlation analysis method, and analyze the initial Euler angle deviation between the main IMU and the turret system to complete the initial alignment of the two IMUs; S3. Initialize the state variables of the enhanced extended Kalman filter with the initial Euler angle deviation, and perform online collaborative calibration for the large lever effect and flexible deformation; S4. Use the rotation matrix from the turret system to the machine system to convert the turret IMU data to the machine system, and fuse the attitude matrix from the machine system to the navigation system, the rotation matrix from the turret system to the machine system, and the azimuth and pitch angles output by the photoelectric turret encoder to calculate the line-of-sight unit vector in the navigation system. S5. Perform lever deformation compensation on the navigation system position data to obtain the compensated and accurate UAV navigation system position; Obtain the distance value from the UAV to the target output by the laser rangefinder, and then perform vector superposition of the compensated UAV position and the result of multiplying the line-of-sight unit vector by the distance value to calculate the WGS-84 geographic coordinates of the target. S6. Based on geographic coordinates and multi-source raw data streams, anti-interference filtering, IMU random error calibration, and turret pitch and tilt angle compensation are performed to provide correction feedback and improve the positioning accuracy of ground targets.

2. The method for ground positioning using a multi-source IMU collaborative calibration of an optoelectronic pod according to claim 1, characterized in that: In step S1, the navigation system adopts the Northeast-Northeast Geographic Coordinate System, with the origin being the UAV takeoff point; the machine system origin is the measurement center of the main IMU, with the X-axis pointing towards the UAV's nose; the turret system origin is the intersection of the azimuth axis and the pitch axis of the photoelectric turret.

3. The method for ground positioning using a multi-source IMU collaborative calibration of an optoelectronic pod according to claim 1, characterized in that: In step S2, the cross-correlation function as follows: ； In the formula, This indicates the time delay, reflecting the phase difference between the two IMU signals; This represents the angular velocity measurement value of the main IMU at time t; Indicates time Angular velocity measurement value of the turret IMU; The initial Euler angle deviation from the turret system to the main system is represented by... The peak position was determined by analysis.

4. The method for ground positioning using a multi-source IMU collaborative calibration of an optoelectronic pod according to claim 1, characterized in that: Step S3 specifically includes: constructing 25-dimensional state variables including navigation system position error, velocity error, attitude angle error, accelerometer bias, gyroscope bias, BeiDou-main IMU arm deformation vector, arm vibration mode angle, main IMU-turret rotation center arm, and turret installation angle error; combining strong vibration characteristics with vibration spectral density modeling process noise covariance matrix to construct EKF state equations that conform to strong vibration and large arm working conditions; substituting multi-source data to complete online real-time estimation of 25-dimensional state variables; solving and compensating for arm elastic deformation and various installation angle errors; and outputting the compensated UAV pose and the attitude matrix from the UAV system to the navigation system.

5. The method for ground positioning using a multi-source IMU collaborative calibration of an optoelectronic pod according to claim 4, characterized in that: 25-dimensional state variables X It is expressed as follows: ； in, Indicates the position error of the navigation system; Indicates the speed error of the navigation system; Indicates the attitude angle error of the navigation system; This indicates that the accelerometer has zero bias. This indicates that the gyroscope has zero bias. This represents the deformation vector of the BeiDou-main IMU arm. , , , These represent the deformation of the lever arm along the x, y, and z axes, respectively. Indicates the modal angle of the lever arm vibration; Indicates the main IMU-turret rotation center arm; This indicates the turret installation angle error.

6. The method for ground positioning using a multi-source IMU collaborative calibration of an optoelectronic pod according to claim 4, characterized in that: Extended Kalman Filter State Equation: ； In the formula, State variables X The rate of change over time; Represents a nonlinear state transition function; The process noise vector is represented by a Gaussian distribution. N ; This represents the process noise covariance matrix, which includes the vibration spectral density parameter.

7. The method for ground positioning using a multi-source IMU collaborative calibration of an optoelectronic pod according to claim 4, characterized in that: In step S4, the formula for calculating the unit vector of the view axis is: ； In the formula, Represents the line-of-sight unit vector in the navigation system; The attitude matrix representing the machine system to the navigation system; Represents the rotation matrix from the turret system to the machine system; and These represent the azimuth and pitch angles output by the photoelectric turret encoder, respectively.

8. The method for ground positioning using a multi-source IMU collaborative calibration of an optoelectronic pod according to claim 7, characterized in that: In step S5, the lever arm deformation compensation formula is as follows: ； In the formula, Indicates the location of the drone's navigation system; For navigation system location data; The attitude matrix representing the machine system to the navigation system; Indicates the nominal lever arm vector of the BeiDou-main IMU; This represents the deformation vector of the BeiDou-main IMU arm; WGS-84 geographic coordinates of the target The solution formula is as follows: ； In the formula, Represents the line-of-sight unit vector in the navigation system; This indicates the distance value from the drone to the target, output by the laser rangefinder.

9. The method for ground positioning using a multi-source IMU collaborative calibration of an optoelectronic pod according to claim 1, characterized in that: Step S6 includes the following sub-steps: S61. Anti-interference multi-source fusion filtering: Extract the observation innovation vector at time k in the filtering process, define the innovation adaptive factor, construct an adaptive Kalman filter model and incorporate the factor, complete the full data filtering optimization, and obtain accurate data after eliminating interference; S62. Online calibration of random errors of dual IMUs: Based on the turret IMU data stream output by S61, the Allan variance analysis method is used to extract angular velocity measurement data, calculate the Allan standard deviation and separate various noise parameters, estimate the noise parameters through curve fitting, and perform zero-bias drift suppression and correction on the dual IMU measurement data. S63. Tilt Compensation Turret Pointing: Based on the zero-bias instability parameters output by S62, the gravity vector under the turret system is obtained by measuring with the turret IMU, the unit vector of the turret system Z-axis is extracted, and the pitch angle compensation is obtained by dot product, ratio and arctangent calculation. The accurate pitch angle is obtained by adding it to the original pitch angle and feeding it back to S4 to recalculate the line-of-sight pointing vector.

10. The method for ground positioning using a multi-source IMU collaborative calibration of an optoelectronic pod according to claim 9, characterized in that: In step S61, the formula for solving the Kalman gain matrix is: ； In the formula, Represents the Kalman gain matrix; Represents the state prediction covariance matrix; Represents the observation matrix; Represents the observation noise covariance matrix; Indicates the information adaptive factor. , This represents the observation information vector at time k; In step S62, the Allan standard deviation formula is: ； In the formula, Indicates the Allan standard deviation; Indicates the relevant time; Indicates the quantization noise figure; This indicates a random walk at an angle; This indicates zero-bias instability; This indicates a rate random walk; Indicates a rate ramp; In step S63, the compensated pitch angle The calculation formula is as follows: ； In the formula, Indicates the compensated pitch angle; This represents the gravity vector (m / s) measured by the turret IMU. 2 ), , These represent the components of the gravity vector along the x, y, and z coordinate axes, respectively. This represents the unit vector along the Z-axis of the turret system.

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