Rapid radar scattering test method based on polarization decomposition

Abstract

The invention belongs to the technical field of radar signal processing, and discloses a radar scattering rapid test method based on polarization decomposition. Comprising the following steps: scanning a target 3D model, simulating scattered field distribution in different incident angles and polarization modes, calculating polarization contrast, and identifying a polarization sensitive area; establishing a dynamic coordinate system conversion model, fusing RTK data, IMU data and laser tracking data, and correcting the position of the trolley and the model in combination with a Doppler frequency shift model and a least square method; calculating an optimal test path of the trolley according to the sensitive area by adopting a natural heuristic optimization algorithm; establishing a geometric environment model, obtaining a back scattering signal through beam forming, analyzing a polarization coherence matrix, obtaining a high-entropy region, and decomposing scattering components; according to the method, closed-loop optimization is formed from a data acquisition source to a scattering component analysis tail end, and the test efficiency and the real-time performance are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of radar signal processing, and more specifically, to a rapid radar scattering measurement method based on polarization decomposition. Background Art

[0002] Current radar cross-section (RCS) measurements mainly rely on a static rotating platform in a microwave anechoic chamber, achieving multi-angle measurements through the rotation of the target itself. However, this method is difficult to apply to large fixed targets such as aircraft and radar vehicles, and the limitations of the anechoic chamber space and cost result in insufficient test flexibility. In outdoor real scenarios, large targets cannot move, making it difficult to directly apply traditional methods; at the same time, multipath reflections, ground clutter, and motion errors in complex environments will reduce the measurement accuracy.

[0003] Chinese Patent Application No. CN116224271A discloses a method for detecting weak targets on the sea surface based on polarization scattering characteristics: Step 1, respectively receive radar echo data of four polarization channels from the sea surface to form a polarization scattering matrix; Step 2, through Cameron decomposition, perform polarization feature decomposition on the signal echo samples according to the characteristics of the polarization scattering matrix. For any polarization scattering matrix, perform a similarity operation with the test scattering matrix to determine whether it belongs to the same type of scattering; Step 3, based on the scattering type differences between clutter and target units, propose the scattering weights of Cameron polarization decomposition; Step 4, calculate the detection rate and false alarm rate based on the scattering weights of Cameron polarization decomposition.

[0004] Although the above method can meet most scenarios, through research and practical application of the above method and the existing technology, it is found that the above method and the existing technology have at least the following partial defects:

[0005] Traditional polarization decomposition methods decompose the scattering mechanism into different physical components through mathematical modeling, which requires a large number of matrix operations, eigenvalue decompositions, etc. on radar echo data. This restricts the application effect of traditional polarization decomposition methods in detecting weak targets on the sea surface and affects the detection rate and real-time performance.

[0006] In view of this, the present invention proposes a rapid radar scattering measurement method based on polarization decomposition to solve the above problems. Summary of the Invention

[0007] In order to overcome the above defects of the existing technology and achieve the above object, the present invention provides the following technical solution: A rapid radar scattering measurement method based on polarization decomposition, including the following steps:

[0008] Scan to obtain the 3D model of the target, simulate the scattering field distribution on the surface of the 3D model of the target under different incident angles and polarization modes, and identify the polarization-sensitive regions on the target surface;

[0009] Establish a dynamic coordinate system conversion model for the trolley position coordinate system and the target coordinate system, obtain RTK data, IMU data, and laser tracking data, and combine the Doppler frequency shift model and the least squares method to obtain and correct the trolley position and the dynamic coordinate system conversion model;

[0010] Adopt a nature-inspired optimization algorithm to calculate the optimal test path of the moving trolley according to the polarization-sensitive region on the target surface;

[0011] Establish a geometric environment model of ground reflection and surrounding objects. When the trolley moves along the optimal test path, obtain the backscattering signal of the target through beamforming technology, analyze to obtain the polarization coherence matrix, calculate the scattering entropy and anisotropy of the polarization coherence matrix, obtain the high-entropy region, and perform dynamic scattering component decomposition on the high-entropy region to obtain the decomposition result.

[0012] Further, the method for identifying the polarization-sensitive region on the target surface includes:

[0013] Preset the radar operating frequency band, polarization mode, incident angle range, and wavelength parameters;

[0014] Obtain the position of the target center point, and discretize and divide the surface of the target's 3D model into W surface grids according to the wavelength constraint;

[0015] Calculate the monostatic RCS values of each surface grid under the four polarization combinations of HH / HV / VH / VV respectively;

[0016] Scan within the preset angle range with a preset step size, and record the polarization scattering matrix at each angle;

[0017] Calculate the polarization contrast based on the ratio of the maximum monostatic RCS and the minimum monostatic RCS under the four polarization combinations;

[0018] Mark the region where the polarization contrast is greater than the preset contrast threshold as the polarization-sensitive region.

[0019] Further, in the method for identifying the polarization-sensitive region on the target surface, the curvature of the surface of the target's 3D model is also calculated, and the local surface grid is encrypted for the region where the curvature change rate is greater than the preset change rate threshold; among them, the side length of a single surface grid and the locally encrypted surface grid both satisfy the corresponding preset wavelength constraint.

[0020] Further, the method for obtaining the high-entropy region includes:

[0021] Obtain the backscattering signal of the target, calculate the covariance of the signal vectors of K independent observations, and obtain the polarization coherence matrix;

[0022] Perform eigenvalue decomposition on the polarization coherence matrix to obtain three non-negative real numbers The formed diagonal matrix, and the unitary matrix formed by the corresponding eigenvectors , and ; The polarization coherence matrix, diagonal matrix and unitary matrix satisfy the matrix constraint conditions;

[0023] The scattering entropy is calculated based on the normalized non - negative real numbers; Calculate and the difference and sum, calculate the ratio of the difference to the sum, and obtain the anisotropy; Mark the region where the scattering entropy is not lower than the preset low - entropy threshold as the high - entropy region.

[0024] Furthermore, the method for obtaining the decomposition result includes:

[0025] Classify the scattering components with normalized non - negative real numbers greater than the preset eigenvalue threshold as surface scattering;

[0026] If the eigenvalue is greater than the preset significant threshold and the anisotropy is greater than the preset anisotropy threshold, then the scattering component is classified as secondary scattering;

[0027] Classify the scattering components with scattering entropy greater than the preset scattering entropy threshold and eigenvalue greater than the preset eigenvalue threshold as volume scattering.

[0028] Furthermore, the method for establishing the dynamic coordinate system transformation model includes:

[0029] Obtain the target coordinate system and the vehicle coordinate system; The target coordinate system takes the target geometric center as the origin and is aligned with the main inertia axis. The vehicle coordinate system takes the radar phase center as the origin, the vehicle motion direction as the X - axis, the horizontal direction perpendicular to the motion direction as the Y - axis, and the vertical direction as the Z - axis; Convert the point coordinates in the vehicle coordinate system to the target coordinate system through the translation vector and rotation matrix.

[0030] Furthermore, the method for obtaining and correcting the vehicle position and the dynamic coordinate system transformation model includes:

[0031] Combine the Doppler frequency - shift model and the least - squares method to update the laser tracking data and obtain the updated laser tracking data;

[0032] Define the state vector to include the position deviation and the attitude deviation. The position deviation is the translation deviation of the radar phase center relative to the target coordinate system; The attitude deviation is the vehicle attitude angle deviation;

[0033] Construct the state transition matrix through the identity matrix, obtain the process noise according to the covariance matrix of the process noise, the covariance matrix of the process noise is set according to the sensor stability, and obtain the predicted state vector according to the state transition matrix and the process noise;

[0034] Calibrate the initial deviation through a laser tracker and set the covariance matrix of the initial state estimation;

[0035] Predict the state at the next moment according to the state transition matrix;

[0036] Obtain the covariance of the predicted updated state estimation according to the state transition matrix, the covariance matrix of the current state estimation, and the covariance matrix of the process noise;

[0037] Obtain the sensor observations composed of RTK data, IMU data, and updated laser tracking data;

[0038] Calculate and obtain the Kalman gain according to the covariance of the predicted updated state estimation, the sensor observation matrix composed of sensor observations, and the covariance matrix of the process noise;

[0039] Update the state estimation according to the covariance matrix of the process noise, the Kalman gain, the sensor observations, and the sensor observation matrix;

[0040] Update the covariance matrix of the state estimation according to the Kalman gain, the sensor observation matrix, and the covariance of the predicted updated state estimation;

[0041] Judge whether the value of the updated covariance matrix is greater than the preset variance threshold. If it is greater, re-update to obtain a new state estimation until the value of the covariance matrix of the updated state estimation is less than the preset variance threshold, and obtain the corresponding updated state estimation. Otherwise, directly obtain the updated state estimation;

[0042] According to the updated state estimation, correct the radar phase center position and attitude angle, update the rotation matrix according to the corrected attitude angle, and correct the trolley position transformation to the target coordinate system according to the updated rotation matrix.

[0043] Further, the method for obtaining the updated laser tracking data includes:

[0044] Obtain the vibration acceleration vector of the IMU data, project the vibration acceleration vector onto the radar line-of-sight direction through the radar line-of-sight direction vector to obtain the effective acceleration;

[0045] Integrate the effective acceleration to obtain the velocity, and combine with the Doppler frequency shift formula to obtain the frequency shift;

[0046] Integrate the effective acceleration twice to obtain the vibration displacement; combine with the radar wavelength to calculate the theoretical phase error;

[0047] Under static scenarios, N groups of values of radar echo phase and theoretical phase error are collected, a time matrix constructed based on the collection time, a polynomial coefficient matrix constructed with polynomial coefficients for modeling the theoretical phase error in polynomial form, and a matrix equation is constructed from a difference matrix obtained from the difference between the radar echo phase and the theoretical phase error;

[0048] Solve the matrix equation to obtain the polynomial coefficient matrix, calculate the actual phase error in real time according to the polynomial coefficient matrix, subtract the actual phase error from the radar echo phase to obtain the true phase; update the laser tracking data according to the true phase to obtain the updated laser tracking data.

[0049] Furthermore, the method for calculating the optimal test path of the mobile trolley includes:

[0050] Discretely divide the test area into P area grids, and assign corresponding area weights to different areas according to the polarization sensitive area;

[0051] Calculate the corresponding fitness according to the area weight and the path length, with the goal of maximizing the total weight of the covered area of the area grid while minimizing the path length;

[0052] Initialize the initial pheromone concentration and pheromone evaporation rate of each area grid;

[0053] Construct a heuristic function according to the area weight and the length between path nodes, calculate the state transition probability according to the heuristic function and the pheromone concentration, and each ant starts from the starting point and selects the next path node according to the probability until it reaches the end point;

[0054] After each ant completes the path, update the pheromone according to the path quality, and the path quality is calculated based on the ratio of the fitness value to the global optimal fitness and the pheromone evaporation rate;

[0055] After each round, enhance the pheromone of the optimal path through the ratio of the fitness to the path length;

[0056] When the preset termination condition is reached, select the optimal path with the highest fitness as the optimal test path.

[0057] Furthermore, the method for obtaining the backscattering signal of the target includes:

[0058] Sample and process the received signal containing only interference and noise, and calculate the covariance matrix of the interference plus noise;

[0059] According to the incident direction of the desired signal, calculate the desired direction steering vector using the array manifold formula;

[0060] Calculate the weight vector according to the covariance matrix of the interference plus noise and the desired direction steering vector;

[0061] Sample and process the received signal containing only multipath interference, calculate the covariance matrix of the received signal, perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and corresponding eigenvectors, after sorting the eigenvalues, form the noise subspace with the eigenvectors corresponding to the eigenvalues below the preset eigenvalue threshold, and calculate the spatial spectrum for each angle within the preset angle range of interest according to the desired direction steering vector and the noise subspace, where the peak of the spatial spectrum corresponds to the direction of the interfering incoming wave;

[0062] For the nth sampling, calculate the a priori error according to the received signal, the desired signal, and the corresponding weight vector; calculate the gain vector according to the error covariance matrix and the received signal; update the weight vector according to the weight vector corresponding to the received signal of the previous sampling, the a priori error, and the gain vector; update the error covariance matrix according to the error covariance matrix corresponding to the received signal of the previous sampling, the gain vector, and the received signal;

[0063] Obtain the covariance matrix loading, add the covariance matrix loading to the diagonal of the covariance matrix of interference plus noise to obtain the adjusted covariance matrix, calculate the beamforming weights through the adjusted covariance matrix, and form the backscattered signal of the target according to the beamforming weights.

[0064] The technical effects and advantages of the fast radar scattering test method based on polarization decomposition of the present invention:

[0065] The present invention uses 3D modeling and electromagnetic simulation to identify polarization-sensitive regions, combines nature-inspired algorithms to plan the optimal path, enables the radar to focus on high-value regions to collect signals, and reduces ineffective scans and redundant data; secondly, dynamically corrects the coordinate system through multi-source data fusion and Kalman filtering to ensure millimeter-level positioning accuracy and avoid signal misalignment and repeated verification caused by platform vibration or coordinate deviation; furthermore, constructs a geometric environment model and combines beamforming to suppress multipath interference, improves the signal-to-noise ratio of the signal, reduces the noise interference and the number of iterative corrections in subsequent polarization decomposition; finally, a fast classification method based on scattering entropy and eigenvalue threshold skips the full matrix decomposition of low-contribution regions and reduces the computational complexity; the present invention forms a closed-loop optimization from the data acquisition source to the end of scattering component analysis, significantly improves the test efficiency and real-time performance, and at the same time ensures the accuracy of polarization scattering characteristic analysis, providing an efficient and reliable technical path for the rapid detection and recognition of targets in dynamic scenarios. Brief Description of the Drawings

[0066] Figure 1 Schematic diagram of the fast radar scattering test method based on polarization decomposition of the present invention;

[0067] Figure 2 Schematic diagram of the data flow of Embodiment 1 of the present invention;

[0068] Figure 3 Schematic diagram of the optimized method for the feedback of the path of the vehicle detouring in the present invention;

[0069] Figure 4 Schematic diagram of the data flow direction in Embodiment 2 of the present invention. Specific implementation manners

[0070] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0071] Embodiment 1

[0072] Please refer to Figure 1 、 Figure 2 As shown, this embodiment provides a fast radar scattering test method based on polarization decomposition, including:

[0073] Scanning to obtain the 3D model of the target, simulating the scattering field distribution on the surface of the 3D model of the target under different incident angles and polarization modes, and identifying the polarization-sensitive regions on the surface of the target;

[0074] The method for identifying the polarization-sensitive regions on the surface of the target includes:

[0075] Presetting the radar operating frequency band, polarization mode, incident angle range, and wavelength parameters;

[0076] Using the finite-difference time-domain method for full-wave electromagnetic simulation;

[0077] Obtaining the position of the center point of the target, discretely dividing the surface of the 3D model of the target according to a preset dimension to form W surface grids, and the side length of a single surface grid satisfies the wavelength constraint, such as the side length of a single surface grid ; where is the radar operating wavelength; calculating the curvature of the surface of the 3D model of the target, and local surface grid encryption can also be performed on the regions with sudden curvature changes (such as the curvature change rate being greater than the preset change rate threshold), and the side length of the surface grid in the corresponding encrypted region .

[0078] For each surface grid of the 3D model of the target, calculate the monostatic RCS values under four polarization combinations of HH / HV / VH / VV respectively; where HH polarization means transmitting a horizontally polarized wave and receiving a horizontally polarized echo; HV polarization means transmitting a horizontally polarized wave and receiving a vertically polarized echo; VH polarization means transmitting a vertically polarized wave and receiving a horizontally polarized echo; VV polarization means transmitting a vertically polarized wave and receiving a vertically polarized echo.

[0079] Perform angular scanning within a preset angular range (such as 0° to 360°) with a step size ≤ 1°, and record the polarization scattering matrix at each angle; such as the Jones matrix or Mueller matrix.

[0080] Calculate the polarization contrast based on the ratio of the maximum and minimum values of the monostatic RCS for four polarization combinations.

[0081] Mark the region where the polarization contrast is greater than the preset contrast threshold as the polarization-sensitive region.

[0082] Identify the polarization-sensitive region by scanning to obtain the target 3D model and simulating the scattering field distribution under different conditions. For the rapid radar scattering test based on polarization decomposition, it can clarify the region on the target surface that responds significantly to the polarization signal, laying a foundation for subsequent targeted acquisition of scattering signals and analysis of the target polarization characteristics, and improving the test efficiency and accuracy. In solving the problems of high computational complexity and insufficient real-time performance of traditional polarization decomposition methods, accurately identifying the polarization-sensitive region can reduce unnecessary computational volume, focus on key regions for analysis, and avoid redundant calculations for the entire target model; at the same time, determining the sensitive region in advance helps optimize the test process, making it more targeted in the signal acquisition and processing links, reducing the amount of data processing, and thus enhancing the real-time performance.

[0083] Establish a dynamic coordinate system transformation model between the trolley position coordinate system and the target coordinate system, obtain RTK data, IMU data, and laser tracking data, and combine the Doppler frequency shift model and the least squares method to obtain and correct the trolley position and the dynamic coordinate system transformation model.

[0084] Establish a dynamic coordinate system transformation model between the trolley position coordinate system and the target coordinate system, combine RTK data (using a dual-frequency RTK module, such as u-blox ZED-F9P, to collect the absolute position of the trolley), IMU data (integrating a six-axis IMU, such as ADIS16470, to measure data such as the vibration acceleration or angular velocity of the trolley in real time), and laser tracking data (using a laser tracker, such as Leica AT960, to perform real-time scanning of the reflection points on the target surface and collect the target attitude), and use the Doppler frequency shift model and the least squares method for correction, which can accurately determine the position and attitude of the trolley relative to the target. This is of great significance for the rapid radar scattering test based on polarization decomposition, providing accurate position information for radar signal acquisition, enabling the antenna to accurately align with the polarization-sensitive region of the target, and improving the accuracy and effectiveness of scattering signal acquisition. At the same time, in solving the problems of complex calculation and poor real-time performance of traditional polarization decomposition methods, accurate position information can optimize the data acquisition range and angle, reduce unnecessary data processing volume, avoid redundant calculations, improve the processing speed, and help achieve rapid testing.

[0085] The method for establishing a dynamic coordinate system conversion model includes:

[0086] Obtain the target coordinate system and the vehicle coordinate system; for example, the target coordinate system has the target geometric center as the origin and is aligned with the main inertial axes (such as the longitudinal axis of the aircraft as the X-axis, the transverse axis as the Y-axis, and the vertical direction as the Z-axis), and the vehicle coordinate system has the radar phase center as the origin, the vehicle movement direction as the X-axis, the horizontal direction perpendicular to the movement direction as the Y-axis, and the vertical direction as the Z-axis; convert the point coordinates in the vehicle coordinate system to the target coordinate system through a translation vector and a rotation matrix.

[0087] Establish a dynamic coordinate system conversion model, clarify the definitions of the origins and axial directions of the target and vehicle coordinate systems, and realize coordinate conversion through a translation vector and a rotation matrix, providing a unified and accurate spatial reference for the rapid radar scattering test based on polarization decomposition, enabling the radar carried by the vehicle to accurately locate the target based on this reference, effectively collect the scattering signals of the polarization-sensitive regions on the target surface, and improving the test efficiency and data accuracy; aiming at the problems of complex calculation and poor real-time performance of traditional polarization decomposition methods, this model reduces redundant calculations and data deviations caused by inconsistent coordinate systems, reduces unnecessary coordinate conversion and error correction steps, reduces the computational complexity, and at the same time ensures the high efficiency of data collection and processing, thereby improving the real-time performance and reliability of the overall test.

[0088] The method for obtaining and correcting the vehicle position and the dynamic coordinate system conversion model includes:

[0089] Update the laser tracking data by combining the Doppler frequency shift model and the least squares method to obtain updated laser tracking data;

[0090] The method for obtaining updated laser tracking data includes:

[0091] Obtain the vibration acceleration vector of the IMU data, project the vibration acceleration vector onto the radar line-of-sight direction through the radar line-of-sight direction vector to obtain the effective acceleration;

[0092] Integrate the effective acceleration to obtain the velocity, and combine the Doppler frequency shift formula to obtain the frequency shift;

[0093] Integrate the effective acceleration twice to obtain the vibration displacement; combine the radar wavelength to calculate the theoretical phase error;

[0094] In a static scenario, collect N groups of values of the radar echo phase and the theoretical phase error, and construct a matrix equation from the time matrix constructed according to the acquisition time, the polynomial coefficient matrix constructed by the polynomial coefficients for modeling the theoretical phase error in polynomial form, and the difference matrix obtained from the difference between the radar echo phase and the theoretical phase error;

[0095] Solve the matrix equation to obtain the polynomial coefficient matrix, calculate the actual phase error in real time according to the polynomial coefficient matrix, subtract the actual phase error from the radar echo phase to obtain the true phase; update the laser tracking data according to the true phase to obtain the updated laser tracking data.

[0096] Define the state vector to include the position deviation and the attitude deviation. The position deviation is the translational deviation of the radar phase center relative to the target coordinate system; the attitude deviation is the deviation of the trolley attitude angles (roll, pitch, yaw).

[0097] Construct the state transition matrix through the identity matrix, obtain the process noise according to the covariance matrix of the process noise, the covariance matrix of the process noise is set according to the sensor stability, and obtain the predicted state vector according to the state transition matrix and the process noise.

[0098] Calibrate the initial deviation through the laser tracker and set the covariance matrix of the initial state estimate.

[0099] Predict the state at the next moment according to the state transition matrix.

[0100] Obtain the covariance of the predicted updated state estimate according to the state transition matrix, the covariance matrix of the current state estimate, and the covariance matrix of the process noise.

[0101] Obtain the sensor observations composed of RTK data, IMU data, and the updated laser tracking data.

[0102] Calculate and obtain the Kalman gain according to the covariance of the predicted updated state estimate, the sensor observation matrix composed of the sensor observations, and the covariance matrix of the process noise.

[0103] Update the state estimate according to the covariance matrix of the process noise, the Kalman gain, the sensor observations, and the sensor observation matrix.

[0104] Update the covariance matrix of the state estimate according to the Kalman gain, the sensor observation matrix, and the covariance of the predicted updated state estimate.

[0105] Judge whether the value of the updated covariance matrix is greater than the preset variance threshold. If it is greater, re-update to obtain a new state estimate until the value of the covariance matrix of the updated state estimate is less than the preset variance threshold, and obtain the corresponding updated state estimate. Otherwise, directly obtain the updated state estimate.

[0106] According to the updated state estimate, correct the position and attitude angles of the radar phase center, update the rotation matrix according to the corrected attitude angles, and convert the trolley position correction to the target coordinate system according to the updated rotation matrix.

[0107] The true phase is obtained by processing the vibration data in the IMU data to update the laser tracking data. The RTK data, IMU data, and laser data are fused with the Kalman filter to achieve accurate estimation of the position and attitude deviation of the trolley and correction of the dynamic coordinate system transformation model, providing position and attitude information with millimeter-level accuracy for the rapid radar scattering test based on polarization decomposition, ensuring the spatio-temporal alignment of the radar phase center and the target coordinate system, enabling the scattering signal acquisition to strictly correspond to the actual azimuth of the target polarization-sensitive area, and avoiding signal acquisition misalignment and data redundancy caused by platform vibration or coordinate deviation. Aiming at the problems of complex calculation and insufficient real-time performance of traditional polarization decomposition methods, this correction method eliminates the cumulative influence of sensor noise and model deviation through multi-source data fusion and dynamic error compensation, reduces the invalid calculation and repeated verification caused by position errors in subsequent polarization decomposition, and improves the data processing efficiency. At the same time, the accurate coordinate transformation provides a reliable benchmark for path planning, enabling the radar to only collect data in the high-value sensitive areas, greatly reducing the data volume and calculation load, and helping to achieve rapid analysis of scattering characteristics with low complexity and high real-time performance.

[0108] A natural inspiration optimization algorithm is adopted to calculate the optimal test path of the mobile trolley according to the polarization-sensitive area on the target surface.

[0109] The method for calculating the optimal test path of the mobile trolley includes:

[0110] The test area is discretely divided into P area grids, and corresponding area weights are assigned to different areas according to the polarization-sensitive area.

[0111] According to the area weight and path length, the corresponding fitness is calculated with the goal of maximizing the total weight of the covered area of the area grid and minimizing the path length at the same time.

[0112] The initial pheromone concentration and pheromone evaporation rate of each area grid are initialized.

[0113] A heuristic function is constructed according to the area weight and the length between path nodes. The state transition probability is calculated according to the heuristic function and the pheromone concentration. Each ant starts from the starting point and selects the next path node according to the probability until it reaches the end point.

[0114] After each ant completes the path, the pheromone is updated according to the path quality, which is calculated based on the ratio of the fitness value to the global optimal fitness and the pheromone evaporation rate.

[0115] After each round, the pheromone of the optimal path is additionally enhanced by the ratio of the fitness to the path length.

[0116] When the preset termination condition is reached, the optimal path with the highest fitness is selected as the optimal test path.

[0117] Adopt a nature-inspired optimization algorithm. By discretizing the test area, assigning weights to polarization-sensitive areas, and constructing a fitness function that includes weights and path lengths, an optimal test path that preferentially covers high-value areas is generated, providing efficient mobile trajectory planning for rapid radar scattering testing based on polarization decomposition. Ensure that the radar focuses on collecting signals from the polarization-sensitive areas on the target surface, reducing ineffective paths and redundant data collection, and significantly improving the test efficiency and the coverage integrity of sensitive areas. Aiming at the problems of complex calculation and insufficient real-time performance of traditional polarization decomposition methods, this path optimization method avoids indiscriminate scanning of low-value areas through heuristic search guided by weights, greatly reducing the amount of data to be processed; combined with a pheromone update strategy to strengthen the optimal path, reducing the search complexity of the algorithm, enabling the radar to complete high-quality data collection in key areas in a short time, providing concise and effective input data for subsequent polarization decomposition, reducing the computational burden from the source of data collection, and improving the real-time performance and efficiency of the overall test process.

[0118] Establish a geometric environment model of ground reflection and surrounding objects. When the trolley moves along the optimal test path, suppress multipath interference through beamforming technology; obtain the backscattering signal of the target, analyze and obtain the polarization coherence matrix, calculate the scattering entropy and anisotropy of the polarization coherence matrix, obtain the high-entropy area, and perform dynamic scattering component decomposition on the high-entropy area to obtain the decomposition result.

[0119] Establish a geometric environment model of ground reflection and surrounding objects and combine beamforming to suppress multipath interference, providing a pure backscattering signal for rapid radar scattering testing based on polarization decomposition. Locate the high-entropy area by accurately constructing the polarization coherence matrix, calculating the scattering entropy and anisotropy, and realize the directional decomposition of the complex scattering components of the target, ensuring the accuracy and pertinence of the scattering characteristic analysis. Aiming at the problems of complex calculation and insufficient real-time performance of traditional polarization decomposition methods, this link improves the signal-to-noise ratio through environmental interference preprocessing, avoids the contamination of the polarization matrix calculation by multipath clutter, and reduces the iterative correction and redundant calculation caused by noise in subsequent decomposition; at the same time, focuses on dynamic component decomposition in the high-entropy area, avoids indiscriminate processing of the whole area, greatly reduces the feature extraction and matrix operation amount, improves the efficiency from both the signal acquisition and data processing links, and helps to achieve rapid radar scattering testing with low complexity and high real-time performance.

[0120] The methods for obtaining the backscattering signal of the target include:

[0121] Sample and process the received signal containing only interference and noise, and calculate the covariance matrix of interference plus noise.

[0122] According to the incident direction of the desired signal, calculate the steering vector of the desired direction using the array manifold formula.

[0123] The weight vector is calculated based on the covariance matrix of interference plus noise and the steering vector of the desired direction.

[0124] Sample and process the received signal containing only multipath interference, calculate the covariance matrix of the received signal, perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and corresponding eigenvectors. After sorting the eigenvalues, the eigenvectors corresponding to the eigenvalues below the preset eigenvalue threshold are spanned to form a noise subspace. In the preset angle range of interest (such as 0° - 360°), calculate the spatial spectrum for each angle according to the steering vector of the desired direction and the noise subspace. Among them, the peak of the spatial spectrum corresponds to the direction of the interfering incoming wave.

[0125] For the nth sampling, calculate the a priori error according to the received signal, the desired signal, and the corresponding weight vector; calculate the gain vector according to the error covariance matrix and the received signal; update the weight vector according to the weight vector corresponding to the received signal of the previous sampling, the a priori error, and the gain vector; update the error covariance matrix according to the error covariance matrix corresponding to the received signal of the previous sampling, the gain vector, and the received signal.

[0126] Obtain the covariance matrix loading, add the covariance matrix loading to the diagonal of the covariance matrix of interference plus noise to obtain an adjusted covariance matrix, calculate the beamforming weight through the adjusted covariance matrix, and form the backscattering signal of the target according to the beamforming weight.

[0127] By calculating the covariance matrix of interference plus noise, the steering vector, and iteratively updating the weight vector, combining spatial spectrum estimation to locate the direction of the interfering incoming wave and adjusting the beamforming weight, provide a backscattering signal with high signal-to-noise ratio for the fast radar scattering test based on polarization decomposition, ensure that the target component dominates in the received signal, avoid the contamination of the polarization coherence matrix calculation by multipath interference, and improve the accuracy of parameter estimation such as scattering entropy and anisotropy. Aiming at the problems of complex calculation and insufficient real-time performance of traditional polarization decomposition methods, this signal acquisition method suppresses multipath interference through preprocessing, reduces repeated iterations and error corrections caused by noise in subsequent polarization decomposition, and reduces the complexity of eigenvalue decomposition and matrix operations; at the same time, precise interference suppression enables the radar to effectively collect the true scattering signal of the target, avoid the processing of invalid data, improve the data quality and processing efficiency from the signal source, and help to achieve fast decomposition of dynamic scattering components with low computational complexity and high real-time performance.

[0128] The methods for obtaining high-entropy regions include:

[0129] Transmit and receive four orthogonal polarization signals through the radar to obtain the backscattering signal of the target. For a single observation, the received signal can be expressed as a three-dimensional polarization vector; calculate the covariance of the signal vectors of K independent observations to obtain the polarization coherence matrix ;

[0130] Perform eigenvalue decomposition on the polarization coherence matrix to obtain a diagonal matrix composed of three non - negative real numbers and a unitary matrix composed of the corresponding eigenvectors , , and ; where , ; among them, is the transpose of the vector; is the identity matrix; the polarization coherence matrix , the diagonal matrix and the unitary matrix satisfy the matrix constraint conditions; such as ; the sum of the eigenvalues is equal to the trace of the polarization coherence matrix;

[0131] Calculate the scattering entropy based on the normalized non - negative real numbers; calculate the difference and sum of and , calculate the ratio of the difference to the sum to obtain the anisotropy; mark the area where the scattering entropy is not lower than the preset low - entropy threshold as the high - entropy area.

[0132] Construct the polarization coherence matrix by transmitting and receiving four orthogonal polarization signals, calculate the scattering entropy and anisotropy through eigenvalue decomposition and mark the high - entropy area, providing accurate positioning of the target complex scattering area for the fast radar scattering test based on polarization decomposition, enabling subsequent dynamic component decomposition to focus on the key areas with rich scattering mechanisms, avoiding indiscriminate processing of the entire area, and improving the analysis efficiency and pertinence. Aiming at the problems of complex calculation and insufficient real - time performance of traditional polarization decomposition methods, this method screens high - value areas through entropy values, greatly reducing the area range to be decomposed and reducing the eigenvalue decomposition and matrix operation amounts; at the same time, based on the covariance calculation and normalization processing of statistical observations, it ensures the robustness of high - entropy area recognition, avoids interference from redundant data, reduces the calculation burden from the data processing level, and helps to achieve efficient and real - time fast analysis of scattering characteristics.

[0133] The method for obtaining the decomposition results includes:

[0134] Classify the scattering components with normalized non - negative real numbers greater than the preset eigenvalue threshold as surface scattering;

[0135] If the eigenvalue is greater than the preset significance threshold and the anisotropy is greater than the preset anisotropy threshold, then the scattering component is classified as double - bounce scattering;

[0136] Classify the scattering components with scattering entropy greater than the preset scattering entropy threshold and eigenvalue greater than the preset eigenvalue threshold as volume scattering.

[0137] Classify the scattering components (surface / secondary / volume scattering) through preset eigenvalues, anisotropy, and scattering entropy thresholds, providing a simple and efficient scattering mechanism identification method for rapid radar scattering testing based on polarization decomposition, enabling the decomposition results to directly correspond to the true physical scattering characteristics of the target (such as surface scattering of a smooth surface, secondary scattering of an angular structure, and volume scattering of vegetation), and avoiding redundant calculations of complex mathematical modeling. Aiming at the problems of complex calculation and insufficient real-time performance of traditional polarization decomposition methods, this classification method focuses on significant scattering components through threshold screening, skipping the ineffective decomposition of low-contribution components, greatly reducing the computational amount of eigenvalue comparison and conditional judgment; at the same time, rapid classification is carried out based on thresholds with clear physical meanings (such as eigenvalues reflecting the energy ratio and scattering entropy characterizing randomness), avoiding the high-complexity operations relying on full matrix decomposition in traditional methods, reducing the computational burden from the end link of scattering mechanism analysis, and helping to achieve rapid decomposition and identification of scattering characteristics with low time consumption and high real-time performance.

[0138] Embodiment 2

[0139] Please refer to Figure 3 、 Figure 4 This embodiment provides a method for optimizing the feedback of the car's detour path applied to Embodiment 1; the method includes the following steps:

[0140] Superimpose the decomposition results, RCS values, and the target 3D model to generate a four-dimensional scattering distribution map (such as a four-dimensional scattering distribution map of time-space-frequency-polarization).

[0141] Mark the scattering components (such as red for secondary scattering and green for volume scattering) and the RCS amplitude on the surface grid of the target 3D model.

[0142] Store the data stratified by the scanning time series and the radar operating frequency band (such as C band, X band), supporting dynamic playback and multi-band comparison.

[0143] Visualize the RCS values of the four polarization channels of HH / HV / VH / VV respectively to form a polarization response heat map.

[0144] Finally, generate an interactive four-dimensional scattering distribution map to display the dynamic changes of the scattering characteristics of each region on the target surface in real time.

[0145] Based on the real-time four-dimensional scattering distribution map, dynamically optimize the car's detour path through a reinforcement learning algorithm.

[0146] Define the state space: including the current car pose (position and attitude in the target coordinate system), the set of covered polarization-sensitive regions, and the key features of the real-time four-dimensional scattering distribution map (such as the positions of uncovered high-entropy regions and regions with missing low RCS values).

[0147] Define the action space: Define the moving directions of the trolley (forward / backward / left / right / rotation), and the next-hop target grid (select the grid that is not covered or has low acquisition accuracy from the polarization-sensitive area).

[0148] Design the reward function:

[0149] Design positive rewards based on covering high-entropy areas, acquiring high-RCS value areas, and short path lengths;

[0150] Design negative penalties based on repeatedly acquiring low-weight areas and entering obstacle areas.

[0151] Iteratively obtain the dynamically optimized detour path of the trolley through the Deep Q-Network (DQN) or the policy gradient algorithm.

[0152] As mentioned above, the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0153] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should all be included in the protection scope of the present invention.

Claims

1. A rapid radar scattering measurement method based on polarization decomposition, characterized in that Including: Scanning to obtain the 3D model of the target, simulating the scattering field distribution on the surface of the 3D model of the target under different incident angles and polarization modes, and identifying the polarization-sensitive regions on the target surface; Establishing a dynamic coordinate system conversion model between the trolley position coordinate system and the target coordinate system, obtaining RTK data, IMU data and laser tracking data, and combining the Doppler frequency shift model and the least squares method to obtain and correct the trolley position and the dynamic coordinate system conversion model; Adopting a nature-inspired optimization algorithm to calculate the optimal test path of the moving trolley according to the polarization-sensitive regions on the target surface; Establishing a geometric environment model of ground reflection and surrounding objects. When the trolley moves along the optimal test path, obtaining the backscattering signal of the target through beamforming technology, analyzing to obtain the polarization coherence matrix, calculating the scattering entropy and anisotropy of the polarization coherence matrix, obtaining the high-entropy region, and performing dynamic scattering component decomposition on the high-entropy region to obtain the decomposition result.

2. The rapid radar scattering measurement method based on polarization decomposition according to claim 1, wherein The method for identifying the polarization-sensitive regions on the target surface includes: Presetting the radar operating frequency band, polarization mode, incident angle range and wavelength parameters; Obtaining the position of the target center point, and discretizing and dividing the surface of the 3D model of the target into W surface grids according to the wavelength constraint; Calculating the monostatic RCS values of each surface grid under the four polarization combinations of HH / HV / VH / VV respectively; Scanning within the preset angle range with a preset step size, and recording the polarization scattering matrix at each angle; Calculating the polarization contrast based on the ratio of the maximum monostatic RCS value and the minimum monostatic RCS value of the four polarization combinations; Marking the regions with polarization contrast greater than the preset contrast threshold as polarization-sensitive regions.

3. The rapid radar scattering measurement method based on polarization decomposition according to claim 2, wherein, In the method for identifying the polarization-sensitive regions on the target surface, the curvature of the surface of the 3D model of the target is also calculated, and the local surface grids are encrypted for the regions with curvature change rate greater than the preset change rate threshold; wherein, the side length of a single surface grid and the locally encrypted surface grids both satisfy the corresponding preset wavelength constraints.

4. The rapid radar scattering measurement method based on polarization decomposition according to claim 1, wherein The method for obtaining the high-entropy region includes: Obtaining the backscattering signal of the target, calculating the covariance of the signal vectors of K independent observations, and obtaining the polarization coherence matrix; Perform eigenvalue decomposition on the polarization coherence matrix to obtain a diagonal matrix composed of three non-negative real numbers , , and a unitary matrix composed of the corresponding eigenvectors , and ; the polarization coherence matrix, the diagonal matrix, and the unitary matrix satisfy matrix constraint conditions; The scattering entropy is obtained based on the calculation of normalized non - negative real numbers; calculate and Calculate the difference and sum of them, calculate the ratio of the difference to the sum to obtain the anisotropy; mark the region where the scattering entropy is not lower than the preset low - entropy threshold as the high - entropy region.

5. The rapid radar scattering measurement method based on polarization decomposition according to claim 4, characterized in that The method for obtaining the decomposition result includes: Classifying the scattering components with normalized non-negative real numbers greater than the preset eigenvalue threshold as surface scattering; If the eigenvalue is greater than a preset significant threshold and the anisotropy is greater than a preset anisotropy threshold, the scattering component is classified as secondary scattering; Classify the scattering components with scattering entropy greater than a preset scattering entropy threshold and eigenvalue greater than a preset eigenvalue threshold as volume scattering.

6. The rapid radar scattering measurement method based on polarization decomposition according to claim 1, characterized in that The method for establishing the dynamic coordinate system conversion model includes: Obtaining the target coordinate system and the trolley coordinate system; the target coordinate system takes the geometric center of the target as the origin and is aligned with the main inertial axis, the trolley coordinate system takes the radar phase center as the origin, the trolley movement direction is the X-axis, the horizontal direction perpendicular to the movement direction is the Y-axis, and the vertical direction is the Z-axis; converting the point coordinates in the trolley coordinate system to the target coordinate system through a translation vector and a rotation matrix.

7. The rapid radar scattering measurement method based on polarization decomposition according to claim 6, characterized in that, The method for obtaining and correcting the trolley position and the dynamic coordinate system conversion model includes: Updating the laser tracking data by combining the Doppler frequency shift model and the least squares method to obtain the updated laser tracking data; Defining the state vector to include the position deviation and the attitude deviation, the position deviation is the translation deviation of the radar phase center relative to the target coordinate system; the attitude deviation is the trolley attitude angle deviation; Construct the state transition matrix through the identity matrix, obtain the process noise according to the covariance matrix of the process noise, where the covariance matrix of the process noise is set according to the sensor stability, and obtain the predicted state vector according to the state transition matrix and the process noise; Calibrate the initial deviation through a laser tracker and set the covariance matrix of the initial state estimate; Predict the state at the next moment according to the state transition matrix; Obtain the covariance of the predicted updated state estimate according to the state transition matrix, the covariance matrix of the current state estimate, and the covariance matrix of the process noise; Obtain the sensor observations composed of RTK data, IMU data, and updated laser tracking data; Calculate and obtain the Kalman gain according to the covariance of the predicted updated state estimate, the sensor observation matrix composed of the sensor observations, and the covariance matrix of the process noise; Update the state estimate according to the covariance matrix of the process noise, the Kalman gain, the sensor observations, and the sensor observation matrix; Update the covariance matrix of the state estimate according to the Kalman gain, the sensor observation matrix, and the covariance of the predicted updated state estimate; Judge whether the value of the updated covariance matrix is greater than the preset variance threshold. If it is greater, re-update to obtain a new state estimate until the value of the covariance matrix of the updated state estimate is less than the preset variance threshold, and obtain the corresponding updated state estimate. Otherwise, directly obtain the updated state estimate; According to the updated state estimate, correct the radar phase center position and the attitude angle, update the rotation matrix according to the corrected attitude angle, and convert the correction of the vehicle position to the target coordinate system according to the updated rotation matrix.

8. The rapid radar scattering measurement method based on polarization decomposition according to claim 7, characterized in that, The method for obtaining the updated laser tracking data includes: Obtain the vibration acceleration vector of the IMU data, project the vibration acceleration vector onto the radar line-of-sight direction through the radar line-of-sight direction vector to obtain the effective acceleration; Integrate the effective acceleration to obtain the velocity, and combine with the Doppler frequency shift formula to obtain the frequency shift; Integrate the effective acceleration twice to obtain the vibration displacement; combine with the radar wavelength to calculate the theoretical phase error; In a static scenario, collect N groups of values of the radar echo phase and the theoretical phase error, and construct a matrix equation from the time matrix constructed according to the acquisition time, the polynomial coefficient matrix constructed for modeling the theoretical phase error in polynomial form, and the difference matrix obtained from the difference between the radar echo phase and the theoretical phase error; Solve the matrix equation to obtain the polynomial coefficient matrix, calculate the actual phase error in real time according to the polynomial coefficient matrix, subtract the actual phase error from the radar echo phase to obtain the true phase; update the laser tracking data according to the true phase to obtain the updated laser tracking data.

9. The rapid radar scattering measurement method based on polarization decomposition according to claim 1, characterized in that The method for calculating the optimal test path of the moving vehicle includes: Discretely divide the test area into P regional grids and assign corresponding regional weights to different regions according to the polarization sensitive area; Calculate the corresponding fitness according to the regional weight and the path length, with the goal of maximizing the total weight of the covered area of the regional grids while minimizing the path length; Initialize the initial pheromone concentration and pheromone evaporation rate of each regional grid; Construct a heuristic function based on the regional weight and the length between path nodes, calculate the state transition probability according to the heuristic function and the pheromone concentration. Each ant starts from the starting point and selects the next path node according to the probability until it reaches the end point; After each ant completes the path, update the pheromone according to the path quality, which is calculated based on the ratio of the fitness value to the global optimal fitness and the pheromone evaporation rate; After each round, enhance the pheromone of the optimal path by the ratio of the fitness to the path length; When the preset termination condition is reached, select the optimal path with the highest fitness as the optimal test path.

10. The rapid radar scattering measurement method based on polarization decomposition according to claim 1, wherein The methods for obtaining the backscattering signal of the target include: Sample and process the received signal containing only interference and noise, and calculate the covariance matrix of the interference plus noise; According to the incident direction of the desired signal, calculate the desired direction steering vector using the array manifold formula; Calculate the weight vector based on the covariance matrix of the interference plus noise and the desired direction steering vector; Sample and process the received signal containing only multipath interference, calculate the covariance matrix of the received signal, perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues and corresponding eigenvectors. After sorting the eigenvalues, the eigenvectors corresponding to the eigenvalues below the preset eigenvalue threshold are spanned into the noise subspace. In the preset angle range of interest, calculate the spatial spectrum for each angle according to the desired direction steering vector and the noise subspace. Among them, the peak of the spatial spectrum corresponds to the incoming wave direction of the interference; For the nth sampling, calculate the a priori error according to the received signal, the desired signal, and the corresponding weight vector; calculate the gain vector according to the error covariance matrix and the received signal; update the weight vector according to the weight vector corresponding to the received signal of the previous sampling, the a priori error, and the gain vector; update the error covariance matrix according to the error covariance matrix corresponding to the received signal of the previous sampling, the gain vector, and the received signal; Obtain the covariance matrix loading, add the covariance matrix loading to the diagonal of the covariance matrix of the interference plus noise to obtain the adjusted covariance matrix, calculate the beamforming weight through the adjusted covariance matrix, and form the backscattering signal of the target according to the beamforming weight.

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