A jitter compensation method and compensation system for a vehicle-mounted miniaturized high-speed ground penetrating radar

Abstract

The present invention provides a jitter compensation method and compensation system for a miniaturized high-speed ground-penetrating radar mounted on a vehicle frame, which relates to the field of ground-penetrating radar detection technology, including a data processor receiving tilt posture information, calculating the tilt angle of the frame, a compensation processor receiving the tilt angle of the frame, and the compensation processor performing adaptive noise and angle threshold adjustment based on road conditions and multi-model switching Kalman filter processing on the frame tilt angle information, predicting the posture information of the frame platform at the next moment and updating the angle threshold; at the same time, the compensation processor generates a compensation motion command based on the predicted data and sends it to the displacement controller of a mechanical compensation device; a distance compensation algorithm processes to obtain motion information, and transmits the motion information to a mechanical compensation device to perform compensation actions. The present application utilizes Kalman filter processing, adaptive noise and angle threshold adjustment mechanism, and linear and nonlinear compensation switching mechanism to perform prediction updates, and adaptively adjusts according to road surface changes to improve the accuracy of data acquisition.

Description

Technical Field

[0001] The present application relates to the field of ground penetrating radar detection technology, and more specifically, to a jitter compensation method and compensation system for a vehicle-mounted miniaturized high-speed ground penetrating radar. Background Art

[0002] The ground-penetrating radar carried by vehicles is used to detect road surface structures and defects in real time. In actual applications, the ground-penetrating radar equipment is subject to various interferences generated during vehicle driving, especially the vibration and tilt of the vehicle caused by the uneven road surface, which makes the detection signal of the ground-penetrating radar easily interfered, thereby affecting the accuracy of the data. The jitter generated during vehicle driving can cause signal deviation and distortion. When the vehicle is driving on an uneven road, the vibration of the vehicle body will cause the relative position of the ground-penetrating radar's detection antenna and the ground to change, especially the vertical offset. This change will distort the radar's transmission and reception signals and affect the accurate detection of underground targets; because the changes in the vehicle's tilt angle and vibration amplitude are difficult to accurately estimate in real time, the detection data of the radar system is unstable. In order to cope with the vibration interference during vehicle driving. At present, existing patents focus more on the design of the ground-penetrating radar stabilization device mechanism and software method compensation, such as:

[0003] (1) A Chinese utility model patent with publication number CN220354978U discloses a fixing device for a ground penetrating radar, comprising a mounting platform, a clamping mechanism, a shock absorbing mechanism and a moving mechanism. The clamping mechanism comprises a motor, an output end of the motor is provided with a bidirectional threaded rod, a surface of the bidirectional threaded rod is threadedly connected to a threaded block, an upper end of the threaded block is provided with a clamping block, an interior of the clamping block is clamped with a ground penetrating radar body, and the shock absorbing mechanism comprises a connecting column, an interior of the connecting column is slidably connected to a piston, an upper end of the piston is fixedly connected to a push rod.

[0004] (2) The Chinese invention patent with publication number CN102565769B discloses a method for compensating the error of a ground penetrating radar system. The method uses amplitude-phase equalization technology to equalize the ideal linear frequency modulation signal, and then puts the equalized signal into the ROM of the FPGA so that it can output the ideal linear frequency modulation signal.

[0005] However, existing jitter compensation measures mainly rely on software data processing and fixed installation structures, and their compensation capabilities are limited. It is difficult to dynamically compensate for vehicle vibrations in real time and effectively, and cannot meet the requirements of high precision and high efficiency in modern transportation infrastructure detection. Summary of the invention

[0006] In order to solve the above problems, the technical solution adopted in this application is a jitter compensation method for a miniaturized high-speed ground penetrating radar mounted on a vehicle frame, which is characterized by comprising the following steps:

[0007] S1: receiving the frame posture information collected by the posture detection device through the data processor, processing the posture information to calculate the frame tilt angle, and sending the frame tilt angle to the compensation processor;

[0008] S2: The compensation processor performs adaptive noise and angle threshold adjustment based on road conditions and multi-model switching Kalman filtering on the frame tilt angle information, predicts the posture of the frame platform at the next moment and updates the angle threshold; at the same time, a compensation motion command is generated based on the predicted data and sent to the displacement controller of the mechanical compensation device;

[0009] S3: The displacement controller receives the compensation motion command, obtains motion information through distance compensation algorithm, and transmits the motion information to the mechanical compensation device to perform compensation action, so that the frame structure tends to a horizontal position.

[0010] Optionally, the frame tilt angle information is measured simultaneously by an accelerometer and a gyroscope and calculated by a data processor. The measurement method is to measure the acceleration component of each axis by the accelerometer and calculate the frame tilt angle by the acceleration of the x, y, and z axes:

[0011] ;

[0012] In the formula, θ accel is the frame inclination angle measured and calculated by the accelerometer, α x is the acceleration component of the x-axis, α y is the acceleration component of the y-axis, α z is the acceleration component of the z-axis;

[0013] The frame tilt angle measured by the gyroscope is obtained by integrating the angular velocity measured by the gyroscope:

[0014] ;

[0015] In the formula, is the frame tilt angle measured by the gyroscope at the current moment; is the initial angle; is the angular velocity measured by the gyroscope.

[0016] Optionally, the multi-model switching Kalman filter includes determining whether the tilt angle exceeds an initial angle threshold based on the frame tilt angle information. If it exceeds the threshold, outputting that this is a pothole in the road, and using the collected tilt data information as the model parameters of the unscented Kalman filter; if it does not exceed the threshold, using the collected tilt data information as the model parameters of the linear Kalman filter.

[0017] Optionally, the steps of the linear Kalman filter algorithm are as follows:

[0018] Prediction stage: Use the system state equation to predict the attitude at the next moment through the angular velocity and tilt angle obtained by the accelerometer and gyroscope, and predict the covariance matrix:

[0019] Update phase: Based on the actual observations of the accelerometer and gyroscope, the prediction results are updated, the Kalman gain is calculated, the state estimate is updated, and the error covariance is updated.

[0020] Optionally, the algorithm steps of the unscented Kalman filter are: generate sigma points, generate a set of sigma points with deterministic distribution from the prior mean and covariance of the state; predict the propagation of the sigma points, perform state transfer on each sigma point, and propagate to the next moment; calculate the weights of the sigma sampling points; re-estimate the predicted mean and covariance through the sigma points: bring the above-mentioned re-estimated predicted mean and covariance into the Kalman filter to obtain the unscented Kalman filter result.

[0021] Optionally, the algorithm steps for adjusting the noise and angle thresholds based on road condition adaptiveness are:

[0022] S2-1: Residual definition:

[0023] ;

[0024] In the formula, e k Residual, Z k represents the observation vector, H represents the observation matrix, represents the state estimate;

[0025] S2-2: Monitor residual changes:

[0026] According to the residual mean square error , adaptively adjust process noise and measurement noise;

[0027] S2-3: Adaptive adjustment of the noise matrix:

[0028] Dynamically adjust the noise covariance matrix according to the change in residual size:

[0029] ;

[0030] ;

[0031] in, has a mean of 0 and a covariance of The white noise matrix, Q k and Q k-1 denote the process noise covariance matrix at time k and time k-1, R k and R k-1denote the observation noise covariance matrix at time k and time k-1 respectively, and is the adjustment coefficient, ∈ , ∈ ;

[0032] On the basis of noise adjustment, the prediction and update process of the multi-model switching Kalman filter remains unchanged, but the process noise and measurement noise covariance matrices are changed and adjusted dynamically in each filtering cycle;

[0033] S2-4: Tilt angle threshold adaptive adjustment:

[0034] Monitor the changing trend of the residual mean square error in real time, and the system automatically adjusts the current angle threshold in each filtering cycle:

[0035] ;

[0036] ;

[0037] ;

[0038] in, is the residual interpolation between the current period and the previous period, is the residual of the previous period; if If is positive and increases continuously, it means that the residual is on an upward trend, indicating that the road conditions are becoming more complicated; if If it is negative and continuously decreasing, it means that the residual is decreasing, indicating that the road condition is becoming stable; is the initial tilt angle threshold, The maximum tilt angle is 10°. The minimum tilt angle is 2°; is the high residual threshold, is the low residual threshold, ∈ , ∈ ; To increase the step size factor, To reduce the step size factor, ∈ , ∈ .

[0039] Optionally, the telescopic distance of the motion information obtained by the distance compensation algorithm is calculated by the following formula:

[0040] ;

[0041] ;

[0042] ;

[0043] Among them, θ is the predicted angle information, is the vertical displacement; is the distance between the gyroscope and the center axis of the frame; is the angle between the telescopic rod and the frame; is the initial length of the telescopic rod; is the initial vertical distance between the lower hinge and the horizontal position of the frame; The telescopic distance compensated by the telescopic rod.

[0044] The present application also provides a vehicle-mounted miniaturized high-speed ground penetrating radar jitter compensation system, which is characterized by comprising:

[0045] Attitude detection device: including accelerometer and gyroscope, used to monitor the attitude information of the frame and transmit it to the data processor;

[0046] Data processor: used for receiving the frame posture information collected by the posture detection device, processing the posture information to calculate the inclination angle of the frame, and sending the frame inclination angle to the compensation processor;

[0047] Compensation processor: used to perform adaptive noise and angle threshold adjustment based on road conditions and multi-model switching Kalman filter processing on the frame tilt angle information, predict the posture information of the frame platform at the next moment and update the angle threshold; at the same time, generate compensation motion commands based on the predicted data and send them to the displacement controller of the mechanical compensation device;

[0048] Displacement controller: used to receive compensation motion commands, obtain motion information through distance compensation algorithm, and transmit the motion information to the mechanical compensation device;

[0049] Mechanical compensation device: used to receive motion information, perform compensation actions, and make the frame structure tend to a horizontal position.

[0050] Optionally, the initial angle threshold is 5°, and the mechanical compensation device includes a frame, an auxiliary tire shaft, an auxiliary tire and a telescopic rod. The frame and the auxiliary tire shaft are connected by a telescopic rod and a telescopic spring. One end of the telescopic rod is connected to the frame through a universal joint, and the other end is connected to the auxiliary tire shaft through a lower hinge. The telescopic rod is provided with a displacement controller, and a shock absorbing mechanism is arranged between the frame and the auxiliary tire.

[0051] Optionally, the accelerometer and the gyroscope are arranged at the connection between the telescopic rod and the frame, the frame is provided with a protective cover, and the data processor and the compensation processor are arranged in the protective cover.

[0052] The beneficial effects of the jitter compensation method and compensation system of a vehicle-mounted miniaturized high-speed ground penetrating radar provided in the present application are:

[0053] The vehicle-mounted miniaturized high-speed ground-penetrating radar jitter compensation system provided in the present application uses an accelerometer, a gyroscope, a receiver, etc. to obtain data on the end of the telescopic rod under jitter conditions, analyze motion data, and predict the position of the end of the telescopic rod at the next time point, so as to predict and correct the posture of the vehicle-mounted platform, and further use a connecting rod control device to drive the motor to achieve robust compensation for the platform;

[0054] The jitter compensation method for a miniaturized high-speed ground-penetrating radar mounted on a vehicle frame provided in the present application uses Kalman filtering, adaptive noise and angle threshold adjustment mechanism, and linear and nonlinear compensation switching mechanism to predict and update the posture and initial angle threshold of the vehicle-mounted ground-penetrating radar platform at the next moment, and generates compensation motion commands based on the predicted data to control the telescopic rod for compensation, thereby achieving jitter compensation of the radar equipment during vehicle driving, significantly improving the detection accuracy and anti-interference ability of the ground-penetrating radar system, and flexibly responding to different road conditions. Compared with using only mechanical compensation, it can not only monitor the vibration state of the vehicle in real time, but also make adaptive adjustments according to changes in the road surface, thereby improving the accuracy of data collection, and providing more reliable technical support for road detection and infrastructure maintenance.

[0055] This application can greatly reduce the debugging time of vehicle-mounted ground-penetrating radar equipment, improve the detection accuracy, vibration resistance and adaptability of the vehicle-mounted ground-penetrating radar system, provide a more reliable solution for underground detection under high-speed driving conditions, and has a broad market prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art.

[0057] Figure 1 This is a flow chart of a jitter compensation method for a vehicle-mounted miniaturized high-speed ground penetrating radar according to an embodiment of the present application;

[0058] Figure 2 A perspective view of the front view of the mechanical compensation device of an embodiment of the present application;

[0059] Figure 3 A three-dimensional diagram of a bottom view of a mechanical compensation device according to an embodiment of the present application;

[0060] Figure 4 A three-dimensional diagram of an axonometric view of a mechanical compensation device according to an embodiment of the present application;

[0061] Figure 5 This is a schematic diagram of the principle of calculating the tilt angle of the frame according to an embodiment of the present application;

[0062] Figure 6 This is a schematic diagram of the principle of calculating the telescopic compensation distance of the telescopic rod in an embodiment of the present application.

[0063] Explanation of the reference numerals: 1-protective cover; 10-shock absorbing mechanism; 11-hook; 12-upper connecting plate; 13-hydraulic rod; 14-lower connecting plate; 15-upper hinge; 16-lower hinge; 17-auxiliary tire; 23-data processor; 24-compensation processor; 32-auxiliary tire shaft; 34-universal joint; 35-telescopic rod; 36-displacement controller; 37-telescopic spring; 38-accelerometer; 39-gyroscope; 6-frame. DETAILED DESCRIPTION

[0064] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0065] Example 1

[0066] like Figure 2-Figure 4 As shown, a vehicle-mounted miniaturized high-speed ground-penetrating radar jitter compensation system includes a mechanical compensation device and a posture detection device;

[0067] The mechanical compensation device includes a telescopic rod 35, a displacement controller 36, a telescopic spring 37, a universal joint 34, a lower hinge, a frame 6, an auxiliary tire shaft 32 and a shock absorbing mechanism 10; the frame 6 and the auxiliary tire shaft 32 are connected by two groups of telescopic rods 35 and telescopic springs 37, and the two ends of each telescopic rod 35 are respectively connected to the frame 6 and the auxiliary tire shaft 32 through a universal joint 34 and a lower hinge, and each telescopic rod 35 is provided with a displacement controller 36.

[0068] The shock absorbing mechanism 10 includes a hook 11, an upper connecting plate 12, a hydraulic rod 13, a lower connecting plate 14, and an auxiliary tire 17. The hook 11, the upper connecting plate 12 and the lower connecting plate 14 are fixed to the frame 6 through one end of the hydraulic rod 13, and the other end of the hydraulic rod 13 is fixed to the auxiliary tire 17 through an upper hinge 15 and a lower hinge 16.

[0069] The posture detection device includes an accelerometer 38 and a gyroscope 39; the accelerometer 38 and the gyroscope 39 are arranged at the connection between the end of each telescopic rod 35 and the universal joint 34, a protective cover 1 is arranged above the frame 6, the data processor 23 and the compensation processor 24 are arranged at the center of the bottom surface of the protective cover 1, and the data processor 23 and the compensation processor 24 are connected via a wireless connection or a UDP transmission network cable.

[0070] When the frame is working, the accelerometer 38 and the gyroscope 39 reach a balance, and then the data processor 23 collects data information, and the compensation processor 24 receives and processes the tilt angle information of the data processor 23, and updates the tilt angle threshold information through the improved Kalman filter processing and sends the jitter compensation result to the displacement controller 36 at the same time, thereby controlling each telescopic rod 35 to perform jitter compensation.

[0071] Each telescopic rod set includes a telescopic rod 35 and a telescopic spring 37 .

[0072] The accelerometer 38 and the gyroscope 39 are in a horizontal position when they start working. When the whole structure shakes, the frame will tilt at a certain angle. The tilt angle can be known through the data collected by the accelerometer 38 and the gyroscope 39.

[0073] The accelerometer 38 and the gyroscope 39 are arranged at the connection between the telescopic rod 35 and the frame 6 to obtain the inclination data of the frame 6 in real time; the data processor 23 obtains the inclination information of the overall structure of the platform by collecting the inclination data and calculating the inclination angle, and sends the inclination angle to the compensation processor 24 via wireless transmission or UDP transmission network cable;

[0074] The compensation processor 24 is a high-performance, low-power DSP chip, which is used to receive and process information from the data processor and command the mechanical compensation device to achieve smooth compensation movement;

[0075] When they start working, the accelerometer 38 and the gyroscope 39 are in a horizontal position, and the compensation mechanism remains stationary without any action. During this period, the Kalman filter begins to establish a preliminary motion state model, and the accelerometer 38 and the gyroscope 39 continue to work and begin to collect vehicle motion data. When the overall structure shakes, the frame will tilt at a certain angle. Through the data collected by the accelerometer 38 and the gyroscope 39, the data processor 23 collects the tilt posture information and calculates the tilt angle. The compensation processor 24 determines whether the road condition is complex and switches to a linear or nonlinear Kalman filter. Then, according to the tilt data received at the current moment, the improved The Kalman filter algorithm is used to predict the platform posture at the next moment and the initial angle threshold update, which is used as the threshold benchmark for the platform tilt angle at the next moment. The threshold is adjusted dynamically, and the system can adaptively adjust the noise and obtain accurate tilt angle and vertical displacement estimates; at the same time, the compensation processor 24 generates a compensation command based on the deviation value between the end position of the telescopic rod 35 and the original horizontal position as a parameter, and the vertical rise amplitude is calculated according to the end position of the telescopic rod 35; and the real-time command is input to the displacement controller 36 of the mechanical compensation device through the UDP transmission network cable, and the two telescopic rods 35 are controlled respectively by the displacement controller 36 to perform compensation movement for the jitter of the frame 6, so that the frame of the carrying platform tends to the original horizontal position.

[0076] Example 2

[0077] like Figure 1 As shown, the present application provides a jitter compensation method for a vehicle-mounted miniaturized high-speed ground penetrating radar, comprising the following steps:

[0078] S1: The platform works smoothly, the accelerometer and gyroscope are in a horizontal and stable position, the data processor collects the accelerometer and gyroscope data at the end of the telescopic rod, calculates the tilt angle, and sends the tilt information to the compensation processor through the UDP transmission network cable;

[0079] Accelerometers provide accurate tilt angle information when stationary or in low-speed motion, but may be disturbed in high-speed rotation or vibration environments; gyroscopes provide more stable tilt angle change information in dynamic environments, but may produce errors due to integration drift after long-term operation. Using accelerometers and gyroscopes at the same time takes advantage of their complementarity to improve the overall performance of the system. The multi-model switching Kalman filter sensor combines the data of the two sensors to reduce errors and improve measurement accuracy.

[0080] The attitude detection device obtains data from the end of the telescopic rod through the accelerometer and gyroscope, and further obtains the platform attitude information. When the platform is working stably, the accelerometer can measure the acceleration components of each axis. The acceleration of the x, y, and z axes can be used to calculate the inclination angle of the frame under the static state of the accelerometer:

[0081] ;

[0082] Among them, θ accel is the frame inclination angle measured and calculated by the accelerometer, is the acceleration component of the x-axis, is the acceleration component of the y-axis, is the acceleration component about the z-axis.

[0083] The tilt angle of the frame measured by the gyroscope can be obtained by integrating the angular velocity measured by the gyroscope:

[0084] ;

[0085] in, is the frame tilt angle measured by the gyroscope at the current moment; is the initial angle, usually zero; is the angular velocity measured by the gyroscope.

[0086] After receiving the tilt data from the stability monitoring system, the compensation processor automatically switches between the linear Kalman filter and the unscented Kalman filter according to the road conditions and determines whether the tilt angle exceeds the initial tilt angle threshold. If it exceeds the threshold, it outputs that this is a pothole on the road, and uses the collected tilt data information as the model parameter of the nonlinear Kalman filter; if it does not exceed the threshold, the collected tilt data information is used as the model parameter of the linear Kalman filter;

[0087] The initial tilt angle threshold is 5°.

[0088] S2; the compensation processor performs Kalman filtering based on road condition adaptive noise and angle threshold adjustment and multi-model switching on the frame tilt angle information, predicts the posture information of the frame platform at the next moment and updates the angle threshold, which serves as the threshold reference for the platform tilt angle at the next moment, and dynamically adjusts the threshold; at the same time, the system generates compensation motion commands based on the predicted data, and finally sends the commands to the displacement controller of the mechanical compensation device through the UDP transmission network cable;

[0089] The mathematical model of Kalman filtering based on road condition adaptive noise and angle threshold adjustment and multi-model switching is as follows:

[0090] Two motion models are set in the model, including linear Kalman filtering and nonlinear Kalman filtering. Multi-model switching Kalman filtering judgment conditions: by detecting the output of the accelerometer and gyroscope, if the accelerometer or gyroscope measures a tilt angle exceeding 5° (i.e., the vibration amplitude is large), the system determines that the road condition is complex and switches to nonlinear Kalman filtering to handle nonlinear changes; if the tilt angles measured by the accelerometer and gyroscope do not exceed 5° (i.e., the vibration amplitude is small), the system switches back to the Kalman filtering mode to reduce the calculation complexity and improve the filtering efficiency.

[0091] The linear Kalman filter state variable is defined as:

[0092] ;

[0093] In the formula, ω k represents the angular velocity at time k.

[0094] The linear Kalman filter state equation is as follows:

[0095] ;

[0096] Among them, X k+1 and X k are the actual system states at time k+1 and time k respectively, is the state transfer matrix of the target, has a mean of 0 and a covariance of Gaussian white noise;

[0097] The state transfer matrix A is:

[0098] ;

[0099] In the formula, Indicates the time interval for system status update;

[0100] The observation equation of the linear Kalman filter is:

[0101] ;

[0102] in, For X k The measurement vector of is the observation matrix, has a mean of 0 and a covariance of The white noise matrix of

[0103] The steps of the linear Kalman filter algorithm are as follows:

[0104] Step 1: Prediction phase: Use the system state equation to predict the attitude at the next moment through the angular velocity and tilt angle obtained by the accelerometer and gyroscope :

[0105] ;

[0106] represents the system state estimate before time step k, based on the information predicted at time step k-1;

[0107] Prediction covariance matrix:

[0108] ;

[0109] Where P k / k-1 represents the predicted covariance matrix, P k-1 represents the covariance matrix at time step k-1, and Q represents the process noise covariance matrix.

[0110] Step 2: Update phase: Update the prediction results based on the actual observations of the accelerometer and gyroscope:

[0111] Kalman gain:

[0112] ;

[0113] K k represents the Kalman gain, H represents the observation matrix, and R represents the observation noise covariance matrix.

[0114] Update the state estimate:

[0115] ;

[0116] represents the updated state estimate at time step k, z k represents the actual observation value at time step k,

[0117] Update the error covariance:

[0118] ;

[0119] P k represents the covariance matrix after update at time step k, and I is the identity matrix;

[0120] Nonlinear Kalman filtering refers to unscented Kalman filtering (UKF), which is a combination of unscented transform (UT) and standard Kalman filtering system. Through lossless transformation, the nonlinear system equation is applicable to the standard Kalman system under linear assumption. It is equivalent to adding UT transformation on the basis of Kalman filtering. The state variables of unscented Kalman filtering are basically the same as those of Kalman filtering. The state equation of unscented Kalman filtering is:

[0121] ;

[0122] ƒ(X k ) represents the state transition function, which describes how the system state is transferred from time step k to k+1, W k represents process noise.

[0123] The algorithm steps are:

[0124] Step 1: Generate sigma points:

[0125] Generate a set of sigma points with a deterministic distribution from the prior mean and covariance of the states:

[0126] ;

[0127] ;

[0128] χ 0 represents the first sigma point, χ i represents the i-th sigma point, n represents the dimension of the state vector, λ represents the scaling function, P k is the covariance matrix at time step k.

[0129] Step 2: Predict the propagation of sigma points:

[0130] Perform state transfer for each sigma point and propagate to the next moment:

[0131] ;

[0132] represents the value of the i-th sigma point at time step k+1, Represents the value of the i-th sigma point at time step k.

[0133] Step 3: Calculate the weight of the sigma sampling point;

[0134] ;

[0135] ;

[0136] ;

[0137] in, , , , ;

[0138] Represents the mean weight of the first sigma point, represents the covariance weight of the first sigma point, represents the mean weight of the i-th sigma point, represents the covariance weight of the i-th sigma point, α is a parameter used to control the degree of diffusion between the sigma point and the mean, β represents a parameter used to compensate for the high-order terms of the nonlinear function, and κ represents a parameter used to adjust the scaling parameter.

[0139] Step 4: Re-estimate the forecast mean and covariance using the sigma point:

[0140] ;

[0141] .

[0142] represents the mean of the predicted state estimates at time step k+1 given the information at time step k, represents the mean weight of the i-th sigma point, represents the covariance weight of the i-th sigma point, P k+1 / k represents the predicted covariance matrix at time step k+1 given the information at time step k, X k+1 / k represents the predicted state value at time step k+1 based on the available information at time step k, represents the predicted value at the i-th sigma point at time step k+1 given the information at time step k.

[0143] Step 5: Substitute the above results into the Kalman filter to obtain the unscented Kalman filter result.

[0144] The system is based on the Kalman filter algorithm with adaptive noise and angle threshold adjustment, which is used to calculate and compensate the tilt angle of the vehicle when driving on complex roads, and can dynamically adjust the noise covariance matrix. and , and dynamically adjust the tilt angle threshold according to each filtering cycle to flexibly adapt to different road conditions:

[0145] The algorithm steps are:

[0146] Step 1: Residual definition:

[0147] The residual is the difference between the predicted state and the actual observation. The residual is defined as:

[0148] ;

[0149] Step 2: Monitor residual changes:

[0150] According to the residual mean square error , adaptively adjust process noise and measurement noise.

[0151] Step 3: Adaptive adjustment of the noise matrix:

[0152] Dynamically adjust the noise covariance matrix according to the change in residual size:

[0153] ;

[0154] ;

[0155] in, has a mean of 0 and a covariance of The white noise matrix, Q k and Q k-1 denote the process noise covariance matrix at time k and time k-1, R k and R k-1 denote the observation noise covariance matrix at time k and time k-1 respectively, and is the adjustment coefficient. ∈ , ∈Value .

[0156] Based on the noise adjustment, the prediction and update process of the Kalman filter remains unchanged, but the process noise and measurement noise covariance matrices are dynamically adjusted according to the changes in innovation in each filtering cycle, ensuring that the filter can adapt to complex environments in real time.

[0157] Step 4: Adaptive adjustment of tilt angle threshold:

[0158] Monitor the changing trend of the residual mean square error in real time, and the system automatically adjusts the current angle threshold in each filtering cycle:

[0159] ;

[0160] Formula 1: ;

[0161] Formula 2: ;

[0162] in, is the residual interpolation between the current period and the previous period, is the residual of the previous period. If is positive and increases continuously, it means that the residual is on an upward trend, indicating that the road conditions are becoming more complicated; if If it is negative and decreases continuously, it means that the residual is decreasing, indicating that the road condition is becoming stable. is the initial tilt angle threshold, The maximum tilt angle is 10°. The minimum tilt angle is 2°; is the high residual threshold, is the low residual threshold, ∈ , ∈ ; To increase the step size factor, To reduce the step size factor, ∈ , ∈ .

[0163] Formula 1 shows that when the residual mean square error is on an upward trend, it means that the system is in a more severe jitter state. At this time, the tilt angle threshold needs to be increased to reduce the possibility of frequent switching of filters. Formula 2 shows that when the residual is on a downward trend, it means that the system environment is relatively stable. The tilt angle threshold needs to be lowered to increase sensitivity to subtle changes.

[0164] Each time the filter is switched, the new filter needs to be properly initialized, including state estimation and covariance matrix. Improper initialization may cause filter performance degradation or divergence. Frequent switching of filters may cause filter stability and convergence problems. Each filter requires time to adapt to the current motion state. Frequent switching will cause the filter to be unable to stably converge to the optimal state.

[0165] Through the adaptive dynamic threshold adjustment method of each cycle mentioned above, the dynamic adaptability to different road conditions is achieved, the accuracy of filter switching is improved, the misjudgment of instantaneous noise is reduced, and the ground penetrating radar system is more stable and anti-interference in complex environments.

[0166] S3: The displacement controller receives the compensation motion command, obtains motion information through distance compensation algorithm, and transmits the motion information to the mechanical compensation device to perform compensation action, so that the frame structure tends to a horizontal position.

[0167] After receiving the predicted data, the displacement controller of the mechanical compensation device decomposes the motion information to the two telescopic rods through the built-in distance compensation algorithm. When each telescopic rod is extended or contracted, the two telescopic rods complete the compensation movement respectively, so that the frame structure tends to the original horizontal position and the compensation is completed.

[0168] like Figure 5 and Figure 6 As shown, each group of telescopic rods includes two telescopic rods. After the displacement controller of the mechanical compensation device receives the compensation command from the compensation processor, the corresponding telescopic rod compensates the corresponding telescopic distance:

[0169] ;

[0170] ;

[0171] ;

[0172] The above formula is the distance compensation algorithm, where d is the vertical displacement; l n is the distance between the gyroscope and the central axis of the frame, which is a constant value; α is the angle between the telescopic rod 35 and the frame 6; l b is the initial length of the telescopic rod; l c is the initial vertical distance between the lower hinge and the horizontal position of the frame; The telescopic distance compensated by the telescopic rod.

[0173] The other combinations and connection relationships of this embodiment are the same as those of the first embodiment.

[0174] Through the above method, the frame 6 structure can eventually be stabilized, and the effect of the strong shaking of the vehicle-mounted ground penetrating radar during high-speed movement can be overcome, so that the frame 6 structure is in a state of calm movement or even in a state of stillness relative to the ground; overcoming the strong shaking of the vehicle-mounted ground penetrating radar during high-speed movement means that when the frame 6 equipped with the ground penetrating radar shakes on the road surface due to the uneven road surface when working at high speed, the displacement in the vertical direction and a certain tilt angle provided by the present application are generated.

[0175] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A jitter compensation method for a vehicle-mounted miniaturized high-speed ground penetrating radar, characterized in that: The following steps are involved: S1: receiving the frame posture information collected by the posture detection device through a data processor, processing the posture information to calculate the frame tilt angle, and sending the frame tilt angle to a compensation processor; S2: The compensation processor performs road-condition adaptive noise and angle threshold adjustment and multi-model switching Kalman filtering on the frame tilt angle information, predicts the posture of the frame platform at the next moment and updates the angle threshold; and generates a compensation motion command based on the predicted data and sends it to the displacement controller of the mechanical compensation device; S3: The displacement controller receives the compensation motion command, obtains motion information through distance compensation algorithm processing, and transmits the motion information to the mechanical compensation device to perform compensation action, so that the frame structure tends to a horizontal position; The multi-model switching Kalman filter includes judging whether the tilt angle exceeds the initial angle threshold according to the tilt angle information of the frame, and if it exceeds the threshold, outputting that the road surface is pothole-like, and using the collected tilt data information as the model parameter of the unscented Kalman filter; If the threshold is not exceeded, the collected tilt data information is used as the model parameter of the linear Kalman filter; The algorithm steps for adjusting the noise and angle thresholds based on road condition self-adaptation are as follows: S2-1: Residual definition: In the formula, e k Residual, Z k represents the observation vector, H represents the observation matrix, represents the state estimate; S2-2: Monitor residual changes: According to the residual mean square error E[(e k ) 2 ], adaptively adjust process noise and measurement noise; S2-3: Adaptive adjustment of the noise matrix: Dynamically adjust the noise covariance matrix according to the change in residual size: Among them, V k is a white noise matrix with a mean of 0 and a covariance of R, Q k and Q k-1 denote the process noise covariance matrix at time k and time k-1, R k and R k-1 Respectively represent the observation noise covariance matrix at time k and time k-1, α Q and α R is the adjustment coefficient, α Q ∈(0.001, 0.1), α R ∈(0.01, 0.2); On the basis of noise adjustment, the prediction and update process of the multi-model switching Kalman filter remains unchanged, but the process noise and measurement noise covariance matrices are changed and adjusted dynamically in each filtering cycle; S2-4: Tilt angle threshold adaptive adjustment: Monitor the changing trend of the residual mean square error in real time, and the system automatically adjusts the current angle threshold in each filtering cycle: Δσ=E[(e k ) 2 ]-s p ; i thresh =min(θ max ,the thresh +βh(E[(e k ) 2 ]-s h )); i thresh =max(θ min ,the thresh -b l (s l -E[(e k ) 2 ])); Among them, Δσ is the residual interpolation between the current period and the previous period, σ p is the residual of the previous cycle; if Δσ is positive and continuously increases, it means that the residual is on an upward trend, indicating that the road condition has become more complicated; if Δσ is negative and continuously decreases, it means that the residual is decreasing, indicating that the road condition is tending to be stable; θ thresh is the initial tilt angle threshold, θ max The maximum tilt angle is 10°, θ min The minimum tilt angle is 2°; σ h is the high residual threshold, σ l is the low residual threshold, σ h ∈(0.4,0.6),σ l ∈(0.1,0.2); β h To increase the step size coefficient, β l To reduce the step size coefficient, β h ∈(0.01,0.1), β l ∈(0.01,0.05).

2. The jitter compensation method for a vehicle-mounted miniaturized high-speed ground penetrating radar as claimed in claim 1, characterized in that: The frame tilt angle information is measured by an accelerometer and a gyroscope at the same time and calculated by the data processor. The measurement method is that the accelerometer measures the acceleration component of each axis and calculates the frame tilt angle through the acceleration of the x, y, and z axes: In the formula, θ accel is the frame inclination angle measured and calculated by the accelerometer, α x is the acceleration component of the x-axis, α y is the acceleration component of the y-axis, α z is the acceleration component of the z-axis; The frame tilt angle measured by the gyroscope is obtained by integrating the angular velocity measured by the gyroscope: In the formula, θ k is the frame tilt angle measured by the gyroscope at the current moment; θ0 is the initial angle; ω(τ) is the angular velocity measured by the gyroscope.

3. The jitter compensation method for a vehicle-mounted miniaturized high-speed ground penetrating radar as claimed in claim 1, characterized in that: The steps of the linear Kalman filter algorithm are as follows: Prediction stage: Use the system state equation to predict the attitude at the next moment and the covariance matrix through the angular velocity and tilt angle obtained by the accelerometer and gyroscope; Update phase: Based on the actual observations of the accelerometer and gyroscope, the prediction results are updated, the Kalman gain is calculated, the state estimate is updated, and the error covariance is updated.

4. The jitter compensation method for a vehicle-mounted miniaturized high-speed ground penetrating radar as claimed in claim 1, characterized in that: The algorithm steps of the unscented Kalman filter are: generating sigma points, generating a set of sigma points with deterministic distribution from the prior mean and covariance of the state; predicting the propagation of the sigma points, performing state transfer on each sigma point, and propagating it to the next moment; calculating the weight of the sigma sampling point; Re-estimate the predicted mean and covariance through the sigma point: Bring the above re-estimated predicted mean and covariance into the Kalman filter to obtain the unscented Kalman filter result.

5. The jitter compensation method for a vehicle-mounted miniaturized high-speed ground penetrating radar as claimed in claim 2, characterized in that: The telescopic distance of the motion information obtained by the distance compensation algorithm is calculated by the following formula: d=l n sinθ; Δl = dsinα; Among them, θ is the predicted angle information, d is the vertical displacement; l n is the distance between the gyroscope and the central axis of the frame; α is the angle between the telescopic rod and the frame; l b is the initial length of the telescopic rod; l c is the initial vertical distance between the lower hinge and the horizontal position of the frame; Δl is the telescopic distance compensated by the telescopic rod.

6. A vehicle-mounted miniaturized high-speed ground-penetrating radar jitter compensation system, characterized in that: include: Attitude detection device: including accelerometer and gyroscope, used to monitor the attitude information of the frame and transmit it to the data processor; A data processor: used for receiving the frame posture information collected by the posture detection device, processing the posture information to calculate the inclination angle of the frame, and sending the frame inclination angle to the compensation processor; Compensation processor: used to perform road condition adaptive noise and angle threshold adjustment and multi-model switching Kalman filter processing on the frame tilt angle information, predict the posture information of the frame platform at the next moment and update the angle threshold; at the same time, generate compensation motion commands based on the predicted data and send them to the displacement controller of the mechanical compensation device; Displacement controller: used for receiving the compensation motion command, obtaining motion information through distance compensation algorithm, and transmitting the motion information to the mechanical compensation device; Mechanical compensation device: used for receiving the motion information and performing compensation action to make the frame structure tend to a horizontal position; The multi-model switching Kalman filter includes judging whether the tilt angle exceeds the initial angle threshold according to the tilt angle information of the frame, and if it exceeds the threshold, outputting that the road surface is pothole-like, and using the collected tilt data information as the model parameter of the unscented Kalman filter; If the threshold is not exceeded, the collected tilt data information is used as the model parameter of the linear Kalman filter; The algorithm steps for adjusting the noise and angle thresholds based on road condition self-adaptation are as follows: S2-1: Residual definition: In the formula, e k Residual, Z k represents the observation vector, H represents the observation matrix, represents the state estimate; S2-2: Monitor residual changes: According to the residual mean square error E[(e k ) 2 ], adaptively adjust process noise and measurement noise; S2-3: Adaptive adjustment of the noise matrix: Dynamically adjust the noise covariance matrix according to the change in residual size: Among them, V k is a white noise matrix with a mean of 0 and a covariance of R, Q k and Q k-1 denote the process noise covariance matrix at time k and time k-1, R k and R k-1 Respectively represent the observation noise covariance matrix at time k and time k-1, α Q and α R is the adjustment coefficient, α Q ∈(0.001, 0.1), α R ∈(0.01, 0.2); On the basis of noise adjustment, the prediction and update process of the multi-model switching Kalman filter remains unchanged, but the process noise and measurement noise covariance matrices are changed and adjusted dynamically in each filtering cycle; S2-4: Tilt angle threshold adaptive adjustment: Monitor the changing trend of the residual mean square error in real time, and the system automatically adjusts the current angle threshold in each filtering cycle: Δσ=E[(e k ) 2 ]-s p ; i thresh =min(θ max ,the thresh +b h (E[(e k ) 2 ]-s h )); i thresh =max(θ min ,the thresh -b l (s l -E[(e k ) 2 ])); Among them, Δσ is the residual interpolation between the current period and the previous period, σ p is the residual of the previous cycle; if Δσ is positive and continuously increases, it means that the residual is on an upward trend, indicating that the road condition has become more complicated; if Δσ is negative and continuously decreases, it means that the residual is decreasing, indicating that the road condition is tending to be stable; θ thresh is the initial tilt angle threshold, θ max The maximum tilt angle is 10°, θ min The minimum tilt angle is 2°; σ h is the high residual threshold, σ l is the low residual threshold, σ h ∈(0.4,0.6),σ l ∈(0.1,0.2); β h To increase the step size coefficient, β l To reduce the step size coefficient, β h ∈(0.01,0.1), β l ∈(0.01,0.05).

7. The vehicle-mounted miniaturized high-speed ground penetrating radar jitter compensation system as claimed in claim 6, characterized in that: The initial angle threshold is 5°. The mechanical compensation device includes a frame, an auxiliary tire shaft, an auxiliary tire and a telescopic rod. The frame and the auxiliary tire shaft are connected by a telescopic rod and a telescopic spring. One end of the telescopic rod is connected to the frame through a universal joint, and the other end is connected to the auxiliary tire shaft through a lower hinge. The telescopic rod is provided with a displacement controller, and a shock absorbing mechanism is arranged between the frame and the auxiliary tire.

8. The vehicle-mounted miniaturized high-speed ground penetrating radar jitter compensation system as claimed in claim 7, characterized in that: The accelerometer and the gyroscope are arranged at the connection between the telescopic rod and the frame, the frame is provided with a protective cover, and the data processor and the compensation processor are arranged in the protective cover.

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