Distributed radar target speed measurement positioning method and device based on iterative adaptive filtering

Abstract

The invention discloses a distributed radar target speed measurement positioning method based on iterative adaptive filtering, and the method comprises the steps: enabling distributed multi-frequency multi-baseline radar echoes to complete frequency band separation, and enabling each frequency band echo to independently complete SAR imaging; each frequency band independently carries out SAR imaging domain clutter suppression and target detection; estimating a target initial speed as an iteration initial value according to a target detection result in combination with multiple frequency bands; estimating a clutter elevation steering vector according to clutter suppression and target detection results; according to the clutter elevation steering vector estimation result, the elevation difference of the moving target detection position is compensated in an auxiliary mode through the target initial speed of multi-band joint estimation, and a compensated target data vector is obtained; estimating the radial speed of the moving target according to the target data vector and repositioning the target; obtaining a moving target repositioning error according to the difference between a moving target repositioning result and a previous estimation result, and judging a repositioning result; and obtaining a final target radial speed estimation and relocation result according to a moving target relocation result.

Description

Technical Field

[0001] The present application relates to the field of radar positioning technology, and in particular to a distributed radar target speed measurement and positioning method and device based on iterative adaptive filtering. Background Art

[0002] With the continuous advancement of radar technology and the growing demand for applications, radar systems can achieve important tasks such as synthetic aperture radar imaging, interferometry, ground moving target indication, and moving target velocity measurement and positioning by increasing degrees of freedom. Space-based distributed multi-frequency multi-baseline radar systems have a very rich degree of freedom in the time-space-frequency domain, which can achieve high-resolution synthetic aperture radar imaging, high-precision interferometry height measurement, and moving target velocity measurement and positioning, and have great application prospects.

[0003] In the past two decades, the estimation and relocation of moving target radial velocity has been the focus of spaceborne radar research. The single-satellite multi-channel system can use the coherence characteristics between channels to achieve clutter suppression, and further combine with adaptive matched filters to estimate the target radial velocity. my country's Gaofen-3 SAR satellite has a dual-channel ground moving target indication (GMTI) mode, which can achieve high-precision moving target velocity measurement and positioning function. The multi-satellite formation system can flexibly form multiple sets of effective baselines, which is an important way to achieve better minimum detectable speed and higher velocity measurement and positioning accuracy. At the same time, distributed radar uses frequency-division orthogonal waveforms to work in multiple-transmit and multiple-receive mode, which can take into account high-resolution imaging and wide-band beam coverage, and maximize system performance. However, for space-based distributed multi-frequency and multi-baseline radar systems, the terrain interference phase introduced by the mixed baseline configuration is a key factor restricting the system's GMTI processing performance. On the one hand, the terrain interference phase makes the spatial distribution of clutter dependent on elevation, and the problem of clutter non-uniformity is prominent, which leads to the deterioration of clutter suppression performance. On the other hand, the coupling problem of terrain interference phase and moving target radial velocity interference phase leads to serious deterioration of moving target parameter estimation performance. For the problem of clutter inhomogeneity, relevant research work has proposed a robust clutter suppression method based on terrain interferometric phase compensation, which will not be repeated here. For the problem of moving target parameter estimation, predecessors proposed a multi-channel multi-pixel joint processing method, which uses the coherent information of adjacent pixel pairs of the target for height compensation, and can realize the target parameter estimation and positioning functions. Its velocity measurement accuracy is higher than that of the single pixel method. However, the radial velocity of the moving target causes the imaging position to shift, and there is a height deviation between the actual position of the target and the imaging position, which in turn leads to radial velocity estimation deviation. To this end, predecessors have proposed an interferometric phase decoupling strategy based on a nonlinear array structure and a method of iteratively compensating the target elevation interferometric phase using DEM information. However, these two methods must meet the constraints of the nonlinear array system, which brings challenges to system design and signal processing.

[0004] In view of this, how to provide a distributed radar target speed measurement and positioning method based on iterative adaptive filtering to achieve high-precision target radial velocity estimation and relocation has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The embodiments of the present application provide a distributed radar target speed measurement and positioning method based on iterative adaptive filtering, a distributed radar target speed measurement and positioning device based on iterative adaptive filtering, an electronic device and a computer storage medium, which are used to solve the problem of target speed measurement and positioning deviation caused by terrain elevation interference phase coupling under a mixed baseline configuration of a distributed radar system.

[0006] In a first aspect of an embodiment of the present application, a distributed radar target speed measurement and positioning method based on iterative adaptive filtering is provided, comprising:

[0007] Distributed multi-frequency and multi-baseline radar echoes complete frequency band separation, and each frequency band echo independently completes SAR imaging;

[0008] Each frequency band independently performs clutter suppression and target detection in the SAR imaging domain;

[0009] According to the independent target detection results of each frequency band, the target initial speed is estimated by combining multiple frequency bands, and the target initial speed is used as the iteration initial value;

[0010] According to the clutter suppression and the target detection results, estimating the clutter elevation steering vector in the target neighborhood by using an eigenvalue decomposition method for the center frequency band;

[0011] According to the estimation result of the clutter elevation guidance vector, the target initial velocity is estimated by using multi-band joint estimation to assist in compensating the elevation difference of the moving target detection position, so as to obtain a compensated target data vector;

[0012] estimating the radial velocity of the moving target using an adaptive matched filtering algorithm according to the compensated target data vector;

[0013] repositioning the target according to the estimated result of the radial velocity;

[0014] The moving target relocation error is obtained by subtracting the moving target relocation result from the previous estimation result, and the relocation result is judged;

[0015] According to the moving target relocation result, the elevation difference of the moving target detection position is recompensed, and the final target radial velocity estimation and relocation result is obtained through iterative processing.

[0016] In a second aspect of an embodiment of the present application, a distributed radar target speed measurement and positioning device based on iterative adaptive filtering is provided, comprising:

[0017] The imaging module is configured as a distributed multi-frequency multi-baseline radar echo to complete frequency band separation, and each frequency band echo independently completes SAR imaging;

[0018] The detection module is configured to independently perform clutter suppression and target detection in the SAR imaging domain in each frequency band;

[0019] A first estimation module is configured to estimate the target initial speed in combination with multiple frequency bands according to the independent target detection results of each frequency band, and use the target initial speed as an iteration initial value;

[0020] A second estimation module is configured to estimate the clutter elevation steering vector in the target neighborhood using an eigenvalue decomposition method for a center frequency band according to the clutter suppression and the target detection results;

[0021] A compensation module is configured to assist in compensating the elevation difference of the moving target detection position according to the estimation result of the clutter elevation guidance vector and using the target initial velocity estimated by multi-band joint estimation to obtain a compensated target data vector;

[0022] A third estimation module is configured to estimate the radial velocity of the moving target using an adaptive matched filtering algorithm according to the compensated target data vector;

[0023] a repositioning module, configured to reposition the target according to the estimation result of the radial velocity;

[0024] The judgment module is configured to obtain a moving target relocation error by subtracting the moving target relocation result from the previous estimation result, and judge the relocation result;

[0025] The iteration module is configured to re-compensate the elevation difference of the moving target detection position according to the moving target relocation result, and obtain the final target radial velocity estimation and relocation result through iterative processing.

[0026] In a third aspect of an embodiment of the present application, a computing device is provided, including:

[0027] Memory and processor;

[0028] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the distributed radar target speed measurement and positioning method based on iterative adaptive filtering are implemented.

[0029] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores computer-executable instructions. When the instructions are executed by a processor, the steps of the above-mentioned distributed radar target speed measurement and positioning method based on iterative adaptive filtering are implemented.

[0030] The present application provides a distributed radar target speed measurement and positioning method based on iterative adaptive filtering, including: completing frequency band separation with distributed multi-frequency multi-baseline radar echoes, and independently completing SAR imaging with echoes of each frequency band; independently performing SAR imaging domain clutter suppression and target detection with each frequency band; estimating the target initial speed by combining multiple frequency bands according to the independent target detection results of each frequency band, and using the target initial speed as the iteration initial value; estimating the clutter elevation steering vector in the target neighborhood by using the eigenvalue decomposition method for the center frequency band according to the clutter suppression and target detection results; and estimating the clutter elevation steering vector according to the estimation result of the clutter elevation steering vector. The method adopts a multi-band joint estimation method to estimate the initial velocity of the target, and uses the multi-band joint estimation to assist in compensating the elevation difference of the moving target detection position to obtain a compensated target data vector; according to the compensated target data vector, an adaptive matched filtering algorithm is used to estimate the radial velocity of the moving target; according to the estimated result of the radial velocity, the target is relocated; according to the difference between the moving target relocation result and the previous estimation result, a moving target relocation error is obtained, and a relocation result is judged; according to the moving target relocation result, the elevation difference of the moving target detection position is recompensed, and the final target radial velocity estimation and relocation result are obtained through iterative processing.

[0031] The distributed radar target speed measurement and positioning method based on iterative adaptive filtering provided in the embodiment of the present application has the following beneficial effects:

[0032] 1. This application adopts an iterative adaptive matching filtering method to realize radial velocity estimation and repositioning of moving targets of space-based distributed multi-frequency multi-baseline radar. The adoption of technical means based on data estimation and compensation of target elevation guidance vector can significantly improve the accuracy of radial velocity estimation, and overcome the problem of reducing the accuracy of speed measurement and repositioning due to the influence of terrain elevation coupling in the prior art; the use of alternating iterations of target elevation compensation and target positioning solves the problem of mutual coupling between target speed measurement and positioning error and terrain elevation compensation error; the signal processing process involved in the present invention is completely based on observation data, which overcomes the problem of prior art dependence on prior information, and thus solves the problem of speed measurement and positioning error caused by inaccurate prior information.

[0033] 2. The present application uses a method of jointly estimating the target Doppler deviation between multiple frequency bands to obtain the target initial velocity, and uses it as auxiliary information to obtain the initial value of terrain phase compensation, which can effectively weaken the terrain elevation coupling, reduce the error of initial velocity measurement and positioning, and speed up the iterative convergence speed and avoid the divergence of iterative results. The present invention overcomes the problem of large target detection position azimuth offset and inability to converge iterative processing when the target radial velocity is large in the prior art, as well as the problem of low target velocity measurement and positioning accuracy in complex geographical scenes with fast-changing terrain.

[0034] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0036] Figure 1 A schematic diagram of a flow chart of a distributed radar target speed measurement and positioning method based on iterative adaptive filtering provided in an embodiment of the present application;

[0037] Figure 2 A schematic diagram of a flow chart of another distributed radar target speed measurement and positioning method based on iterative adaptive filtering provided in an embodiment of the present application;

[0038] Figure 3 This is a simulation result diagram of space-based multi-frequency and multi-baseline distributed radar SAR image data in a distributed radar target speed measurement and positioning method based on iterative adaptive filtering provided in an embodiment of the present application;

[0039] Figure 4 This is a curve diagram of the change of RMSE of radial velocity estimation of a space-based multi-frequency multi-baseline distributed radar target versus the radial velocity of the target in a distributed radar target velocity measurement and positioning method based on iterative adaptive filtering provided in an embodiment of the present application;

[0040] Figure 5 It is a space-based multi-frequency and multi-baseline distributed mine target relocation result in a distributed radar target speed measurement and positioning method based on iterative adaptive filtering provided in an embodiment of the present application;

[0041] Figure 6 This is a curve showing the change of RMSE of target radial velocity estimation with the number of iterations in a distributed radar target velocity measurement and positioning method based on iterative adaptive filtering provided in an embodiment of the present application;

[0042] Figure 7 It is a SAR image of airborne multi-frequency and multi-baseline distributed radar measured data in a distributed radar target speed measurement and positioning method based on iterative adaptive filtering provided in an embodiment of the present application;

[0043] Figure 8This is a target relocation result diagram after processing the measured data of the airborne multi-frequency multi-baseline distributed radar in a distributed radar target speed measurement and positioning method based on iterative adaptive filtering provided in an embodiment of the present application;

[0044] Fig. 9 A schematic diagram of the structure of a distributed radar target speed measurement and positioning device based on iterative adaptive filtering provided in an embodiment of the present application;

[0045] Fig.10 A structural block diagram of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0047] In a distributed radar system with a mixed baseline configuration, the interference phase between channels simultaneously introduces the terrain interference phase and the moving target radial velocity interference phase. The coupling of the two leads to a serious deterioration in the moving target parameter estimation performance. Therefore, the terrain interference phase of the distributed radar system is a key factor restricting the system's GMTI processing performance.

[0048] In order to solve the problem of moving target parameter estimation in multi-channel radar system, predecessors proposed a multi-channel multi-pixel joint processing method, which uses the coherent information of adjacent pixel pairs of the target to perform height compensation, and can realize the target parameter estimation and positioning functions. Its velocity measurement accuracy is higher than that of the single pixel method. However, the radial velocity of the moving target causes the imaging position to shift, and there is a height deviation between the true position of the target and the imaging position, which in turn leads to radial velocity estimation deviation. To this end, predecessors proposed an interference phase decoupling strategy based on nonlinear array structure and a method of iteratively compensating the target elevation interference phase using digital terrain elevation map information. However, this method must meet the constraints of the nonlinear array system, which brings challenges to system design and signal processing.

[0049] To address the problem of inaccurate parameter estimation of moving targets caused by interferometric phase coupling in distributed radar systems, this application proposes a distributed radar target velocity measurement and positioning method based on iterative adaptive filtering, which can realize target radial velocity estimation and repositioning under a mixed baseline configuration.

[0050] See also Figure 1 , Figure 1 The present invention provides a flow chart of a distributed radar target speed measurement and positioning method based on iterative adaptive filtering. Figure 1As shown, the specific steps include the following steps.

[0051] Step S102: Distributed multi-frequency and multi-baseline radar echoes complete frequency band separation, and each frequency band echo independently completes SAR imaging;

[0052] Step S104: each frequency band independently performs SAR imaging domain clutter suppression and target detection;

[0053] Step S106: according to the independent target detection results of each frequency band, estimate the target initial speed jointly with multiple frequency bands, and use the target initial speed as the iteration initial value;

[0054] Step S108: estimating the clutter elevation steering vector in the target neighborhood using an eigenvalue decomposition method for the center frequency band according to the clutter suppression and target detection results;

[0055] Step S110: according to the estimation result of the clutter elevation guidance vector, using the target initial velocity estimated by multi-band joint estimation, auxiliary compensation of the elevation difference of the moving target detection position is performed to obtain a compensated target data vector;

[0056] Step S112: estimating the radial velocity of the moving target using an adaptive matched filtering algorithm according to the compensated target data vector;

[0057] Step S114: relocating the target according to the estimation result of the radial velocity;

[0058] Step S116: Subtract the moving target relocation result from the previous estimation result to obtain the moving target relocation error, and make a judgment on the relocation result;

[0059] Step S118: re-compensate the elevation difference of the moving target detection position according to the moving target relocation result, and obtain the final target radial velocity estimation and relocation result through iterative processing.

[0060] In an embodiment of the present application, each frequency band independently performs SAR imaging domain clutter suppression and target detection, including: in a distributed multi-frequency multi-baseline radar system configuration, using terrain interferometric phase compensation to eliminate the terrain dependence of clutter; based on the interferometric phase compensation result, using a space-time adaptive algorithm in the SAR image domain to suppress clutter; after clutter suppression, using a constant false alarm detection method to detect the target.

[0061] In practical applications, under the configuration of the space-based distributed multi-frequency multi-baseline radar system, terrain interferometry phase compensation is used to eliminate the terrain dependence of clutter for multi-channel SAR image data of each frequency band.

[0062] It should be noted that the same operation needs to be performed on each frequency band echo to perform target detection.

[0063] In an embodiment of the present application, the method of estimating the clutter elevation steering vector in the target neighborhood using an eigenvalue decomposition method for the center frequency band according to the clutter suppression and the target detection result includes:

[0064] According to the clutter suppression and the target detection results, based on a first target calculation formula, for a center frequency band, an eigenvalue decomposition method is used to estimate a clutter elevation steering vector in a target neighborhood, wherein the first target calculation formula includes:

[0065]

[0066] in, represents the clutter covariance matrix estimated by the clutter sample data in the target area; γ1,…,γ L Represents the eigenvalue, arranged from large to small, the eigenvector corresponding to the largest eigenvalue is the elevation interference phase guide: v1…v L represents the corresponding eigenvector; () T Represents a matrix transpose operation.

[0067] In the embodiment of the present application, the method of estimating the target initial speed by combining multiple frequency bands according to the independent target detection results of each frequency band, and using the target initial speed as the iteration initial value includes:

[0068] The target initial velocity is obtained by using the relationship between the target velocity, the central wavelength of the frequency band and the target Doppler frequency, and adopting the target estimation method, wherein the target initial velocity is the characteristic vector with the minimum error of the Doppler offset of each frequency band.

[0069] It should be noted that the target estimation method here may be a least squares fitting method, or may be an alternative to the least squares fitting method, or may be replaced by other methods, which is not limited in the embodiments of the present application.

[0070] The target velocity can be estimated by combining multiple frequency bands. The least square fitting method can be used to obtain the target velocity estimation result by utilizing the relationship between the target velocity, the center wavelength of the frequency band, and the target Doppler frequency.

[0071] f di =2v r / λ i

[0072] In the formula, f di represents the Doppler frequency of the target in the i-th frequency band, v r represents the target radial velocity, λ i Represents the wavelength of radar signals in i frequency bands.

[0073] In the embodiment of the present application, the estimation result of the clutter elevation guidance vector is used to jointly estimate the target initial velocity with multiple frequency bands to assist in compensating the elevation difference of the moving target detection position to obtain the compensated target data vector, including:

[0074] Determine the real position area of ​​the moving target based on the target initial velocity information estimated by multi-band joint estimation;

[0075] According to the estimation result of the clutter elevation steering vector, using the clutter samples corresponding to the surrounding area of ​​the moving target real position area, estimating the first elevation steering vector of the target real position;

[0076] Calculate the elevation difference guidance vector between the real position of the target and the detection position according to the first elevation guidance vector of the real position of the target and the second elevation guidance vector of the detection position;

[0077] According to the elevation difference guidance vector, a phase compensation process is performed on the target data vector to obtain a compensated data vector.

[0078] Specifically, calculating the elevation difference guidance vector between the real position of the target and the detection position according to the first elevation guidance vector of the real position of the target and the second elevation guidance vector of the detection position includes:

[0079] The calculation formula of the elevation difference guidance vector between the target's real position and the detected position is:

[0080]

[0081] in, Indicates the elevation guidance of the target detection location; Elevation guidance of the target's true position; symbol () * represents the conjugate operation; the symbol represents the Hadamard product.

[0082] Specifically, according to the elevation difference guidance vector, performing phase compensation processing on the target data vector to obtain a compensated data vector includes:

[0083] In one embodiment of the present invention, the elevation difference between the real position of the target and the detected position guides the vector, and the target data vector is subjected to phase compensation processing. The compensated data vector is expressed as:

[0084]

[0085] in, represents the compensated data vector; z represents the data of different channels arranged in columns as vectors z = [z1, z2, …, z L ]; A guidance vector representing the elevation difference between the target's true position and the detected position.

[0086] In the embodiment of the present application, estimating the radial velocity of the moving target using an adaptive matched filtering algorithm according to the compensated target data vector includes:

[0087] According to the compensated target data vector, an adaptive matched filtering algorithm is used to estimate the radial velocity of the moving target, wherein the adaptive matched filtering algorithm includes:

[0088]

[0089] in, represents the radial velocity estimation result after road compensation; represents the guidance vector containing only the target velocity; represents the compensated data vector.

[0090] In the embodiment of the present application, relocating the target according to the estimation result of the radial velocity includes:

[0091]

[0092] Among them, y D Indicates the position corresponding to the target detection result; R T Indicates the target slant distance; v a Indicates the flight speed of the satellite platform.

[0093] In the embodiment of the present application, the moving target relocation result is subtracted from the previous estimation result to obtain the moving target relocation error, and the relocation result is judged, including:

[0094] Based on the decision conditions, the moving target relocation result is subtracted from the previous estimation result to obtain the moving target relocation error, and the relocation result is judged, wherein the decision conditions include:

[0095]

[0096] Among them, v rn Indicates the target radial velocity estimation result in the current iteration; v rn-1 Represents the target radial velocity estimation result in the last iteration; y o Indicates the location of the road centerline; The elevation difference guidance vector representing the target's true position and detected position in the current iteration; The elevation difference guidance vector represents the elevation difference between the target's real position and the detected position in the previous iteration; ε1, ε2 are given constants that can be set according to the system repositioning accuracy.

[0097] In an embodiment of the present application, according to the moving target relocation result, the elevation difference of the moving target detection position is recompensed, and the final target radial velocity estimation and relocation result is obtained through iterative processing, including:

[0098] The moving target relocation result is brought into the step of "using the target initial velocity estimated by multi-band joint to assist in compensating the elevation difference of the moving target detection position" to assist in estimating the target elevation difference;

[0099] The steps of "using the target initial velocity estimated by multi-band joint to assist in compensating the elevation difference of the moving target detection position", "using the adaptive matched filtering algorithm to estimate the radial velocity of the moving target", "using the radial velocity estimation result to relocate the moving target", "subtracting the moving target relocation result from the previous estimation result to obtain the moving target relocation error and judge the relocation result" and so on are repeatedly executed through iterative processing to obtain the final target radial velocity estimation and relocation result.

[0100] See also Figure 2 , Figure 2 A schematic flow chart of another distributed radar target speed measurement and positioning method based on iterative adaptive filtering provided in an embodiment of the present application.

[0101] like Figure 2 As shown, the specific steps include the following steps.

[0102] Step S201: inputting raw echo data of multi-frequency multi-baseline distributed radar;

[0103] Step S202: echo frequency band separation and SAR imaging processing;

[0104] Step S203: clutter suppression and target detection;

[0105] Step S204: jointly estimate the initial value of the target speed by multiple frequency bands;

[0106] Step S205: estimating the elevation guidance in the central frequency band;

[0107] Step S206: initial speed assists in obtaining target elevation deviation;

[0108] Step S207: Adaptively matching and searching to estimate radial velocity;

[0109] Step S208: target relocation;

[0110] Step S209: Calculate the speed measurement and positioning errors of two iterations;

[0111] Step S210: Determine whether the target speed measurement positioning error has converged?

[0112] Step S211: If yes, output the target speed and relocation result;

[0113] Step S212: If not, perform iterative update and re-execute steps S206 to S211.

[0114] The effect of the present invention is further illustrated below through a space-based multi-frequency multi-simulation experiment.

[0115] 1. Simulation conditions.

[0116] In the simulation experiment of this embodiment, the satellite-borne four-star formation SAR-GMTI system is taken as an example, and the simulation parameters are configured as follows: orbit altitude 700km, satellite flight speed 7500m / s, working wavelength 0.03m, pulse repetition rate 4000Hz, distributed radar uses frequency division orthogonal waveform, signal bandwidth 50MHz, frequency band interval 50MHz, target signal-to-noise ratio is 20dB, and clutter noise ratio is 20dB. The distributed radar also includes along-track baselines and vertical-track baselines. The along-track baselines are 0, 40, 80, and 120 meters respectively, and the vertical baselines are 0, 30, 60, and 90 meters respectively. The detailed parameters are shown in Table 1:

[0117] Table 1 Spaceborne radar system parameters

[0118]

[0119] In the measured data experiment of this embodiment, the airborne distributed multi-baseline radar SAR-GMTI system is taken as an example. In order to verify the performance of the multi-band multi-baseline distributed radar system, the echo data is divided into three sub-bands during signal processing. The specific system parameter configuration is shown in Table 2:

[0120] Table 2 Airborne radar system parameters

[0121]

[0122] 2. Simulation content and result analysis.

[0123] Simulation content: This embodiment uses the distributed radar target speed measurement and positioning method based on iterative adaptive filtering provided in the above-mentioned embodiment 1 to complete GMTI processing for the space-based multi-frequency multi-baseline distributed radar simulation data and the airborne multi-frequency multi-baseline distributed radar measured data, respectively, to verify the target radial velocity estimation and repositioning performance of the method of the present invention.

[0124] Result analysis: See Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 .

[0125] Specifically, Figure 3 : is a simulation result diagram of space-based multi-frequency and multi-baseline distributed radar SAR image data in a distributed radar target speed measurement and positioning method based on iterative adaptive filtering provided in an embodiment of the present application, wherein Figure 3 (a) is the DEM map of the simulation scene, (b) is the original amplitude map, and (c) is the interference phase map. It can be clearly seen from the interference phase map that the terrain elevation interference phase changes rapidly along the distance and azimuth dimensions. In this type of observation scenario, there is a large difference between the elevation interference phase of the target's actual position and the imaging position, resulting in target elevation interference phase compensation errors, which in turn affects the radial velocity estimation performance.

[0126] Figure 4 This is a curve chart showing the change of RMSE of radial velocity estimation of space-based multi-frequency and multi-baseline distributed radar target with target radial velocity in a distributed radar target velocity measurement and positioning method based on iterative adaptive filtering provided in an embodiment of the present application. The processing results of the algorithm of the present invention (denoted as: I-AMF algorithm) are compared with the one-dimensional adaptive matching filter algorithm (denoted as: 1D-AMF method), the two-dimensional adaptive matching filter algorithm (denoted as: 2D-AMF method), the alternating iterative maximum likelihood estimation method (denoted as: CDML method), and the speed estimation Cramer-Rao Bound (denoted as: CRB). The simulation experiment is carried out by setting point targets with different radial velocities, performing 100 Monte Carlo experiments, and statistically calculating the root mean square error (denoted as: RMSE) of the velocity estimation, and providing the change curves of the RMSE estimated by different processing methods and the target radial velocity. Figure 4 It is obvious that the AMF method directly used for moving target radial velocity estimation under mixed baseline configuration has a large error, and the larger the target velocity, the larger the RMSE value. This is because the larger the target velocity, the farther its imaging position deviates from the true position, and the larger the elevation interferometry phase compensation error. Figure 3 (a) and (c) show that under the experimental conditions of this example, the larger the target azimuth position offset, the larger the corresponding terrain height difference. The simulation results show that the target terrain elevation interferometric phase compensation error causes the performance of the AMF radial velocity estimation method to deteriorate seriously or even fail. Figure 4 In the figure, the blue solid line is the RMSE curve obtained by the I-AMF method of the present invention, and the black solid line is the Cramer-Rao bound for radial velocity estimation. Figure 4 It can be seen that under the distributed multi-baseline array configuration, the algorithm of the present invention can significantly improve the radial velocity estimation accuracy.

[0127] Figure 5This is the result of the space-based multi-frequency multi-baseline distributed radar target relocation in a distributed radar target speed measurement and positioning method based on iterative adaptive filtering provided in an embodiment of the present application. The blue circle in the figure marks the detected target position, and the yellow plus sign marks the real azimuth position of the target relocation. Figure 5 It can be seen that the moving target deviates from the road in azimuth after imaging due to the radial velocity. After GMTI processing, the moving target is relocated to the road at the same range gate as its detection position. Figure 5 (ad) It is obvious that the traditional one-dimensional and two-dimensional AMF methods are affected by the elevation interference phase compensation error, and the velocity measurement and repositioning performance is deteriorated. The algorithm proposed in the present invention (I-AMF algorithm) can obtain higher target radial velocity estimation accuracy.

[0128] Figure 6 This is a curve of the change of the target radial velocity estimation RMSE with the number of iterations in a distributed radar target velocity measurement and positioning method based on iterative adaptive filtering provided by the embodiment of the present application. The present invention adopts iterative processing to obtain the final target radial velocity estimation and repositioning results, further reducing the influence of the prior information extraction error. Figure 6 It can be seen that the method of the present invention has good robustness to the road network information error and can converge after 3 to 4 iterations.

[0129] Figure 7 It is a SAR image of airborne multi-frequency multi-baseline distributed radar measured data in a distributed radar target speed measurement and positioning method based on iterative adaptive filtering provided in an embodiment of the present application. Figure 7 The magnitude shows that there is a train in the scene and the imaging position is shifted because of the target motion.

[0130] Figure 8 This is a target relocation result diagram after processing the measured data of the airborne multi-frequency multi-baseline distributed radar in a distributed radar target speed measurement and positioning method based on iterative adaptive filtering provided in an embodiment of the present application, (a) 1D-AMF method (b) 2D-AMF method (c) CDML method (d) the method of the present invention. The blue circle marks the detected target position, and the yellow plus sign marks the real azimuth position of the target relocation. Compare Figure 8 The processing results of (ad) clearly show that the traditional one-dimensional and two-dimensional AMF methods and CDML methods are affected by the phase compensation error of elevation interference, and their speed measurement relocation performance deteriorates, and the relocation position is obviously deviated from the road. After GMTI processing using the I-AMF method proposed in the invention, the target is relocated to the rail at the same distance from its detection position. In particular, Figure 8It can be seen from (d) that the number of target points accurately relocated to the railroad track is the largest. This result proves that more accurate radial velocity estimation and relocation results can be obtained after processing using the I-AMF algorithm of the present invention.

[0131] From the simulation experiment results, it can be seen that the I-AMF method of the present invention can effectively eliminate the influence of the elevation interferometry phase compensation error and improve the target radial velocity estimation and relocation performance of the space-based multi-frequency multi-baseline distributed radar system in the terrain undulating scene. The present invention adopts iterative processing to obtain the final target radial velocity estimation and relocation results, which has good convergence and robustness.

[0132] The application of the method provided by the embodiment of the present application has the following beneficial effects: 1. The present application adopts an iterative adaptive matched filtering method to realize the radial velocity estimation and repositioning of moving targets of space-based distributed multi-frequency multi-baseline radar. The technical means based on data estimation and compensation of target elevation guidance vector can significantly improve the accuracy of radial velocity estimation, and overcome the problem of reducing the accuracy of speed measurement and repositioning due to the influence of terrain elevation coupling in the prior art; the alternating iteration of target elevation compensation and target positioning is adopted to solve the problem of mutual coupling between target speed measurement and positioning error and terrain elevation compensation error; the signal processing process involved in the present invention is completely based on observation data, which overcomes the problem of prior art dependence on prior information, and thus solves the problem of speed measurement and positioning error caused by inaccurate prior information. 2. The present application obtains the initial velocity of the target by means of joint estimation of target Doppler deviation between multiple frequency bands, and uses it as auxiliary information to obtain the initial value of terrain phase compensation, which can effectively weaken the terrain elevation coupling, reduce the error of initial speed measurement and positioning, and accelerate the iterative convergence speed and avoid the divergence of iterative results. The present invention overcomes the problems in the prior art that when the radial velocity of the target is large, the azimuth offset of the target detection position is large and the iterative processing cannot converge, and the problem that the target speed measurement and positioning accuracy is low in complex geographical scenes with rapidly changing terrain.

[0133] Corresponding to the above method embodiment, this specification also provides an embodiment of a distributed radar target speed measurement and positioning device based on iterative adaptive filtering, Fig. 9 The present invention provides a structural diagram of a distributed radar target speed measurement and positioning device based on iterative adaptive filtering. Fig. 9 As shown, the device comprises:

[0134] Imaging module 902, configured as distributed multi-frequency multi-baseline radar echo to complete frequency band separation, and each frequency band echo independently completes SAR imaging;

[0135] The detection module 904 is configured to perform SAR imaging domain clutter suppression and target detection independently in each frequency band;

[0136] A first estimation module 906 is configured to estimate the target initial speed in combination with multiple frequency bands according to the independent target detection results of each frequency band, and use the target initial speed as an iteration initial value;

[0137] A second estimation module 908 is configured to estimate the clutter elevation steering vector in the target neighborhood using an eigenvalue decomposition method for the center frequency band according to the clutter suppression and the target detection results;

[0138] The compensation module 910 is configured to assist in compensating the elevation difference of the moving target detection position according to the estimation result of the clutter elevation steering vector and using the target initial velocity estimated by multi-band joint estimation to obtain a compensated target data vector;

[0139] A third estimation module 912 is configured to estimate the radial velocity of the moving target using an adaptive matched filtering algorithm according to the compensated target data vector;

[0140] A repositioning module 914, configured to reposition the target according to the estimation result of the radial velocity;

[0141] The decision module 916 is configured to make a decision on the relocation result by subtracting the moving target relocation result from the previous estimation result to obtain a moving target relocation error;

[0142] The iteration module 918 is configured to re-compensate the elevation difference of the moving target detection position according to the moving target relocation result, and obtain the final target radial velocity estimation and relocation result through iterative processing.

[0143] In an optional embodiment, the detection module 904 is further configured to:

[0144] In the configuration of distributed multi-frequency multi-baseline radar system, terrain interferometry phase compensation is used to eliminate the terrain dependence of clutter;

[0145] According to the interferometric phase compensation results, the space-time adaptive algorithm in SAR image domain is used to suppress clutter.

[0146] After clutter suppression, the constant false alarm detection method is used to detect the target.

[0147] In an optional embodiment, the second estimation module 908 is further configured to:

[0148] According to the clutter suppression and the target detection results, based on a first target calculation formula, for a center frequency band, an eigenvalue decomposition method is used to estimate a clutter elevation steering vector in a target neighborhood, wherein the first target calculation formula includes:

[0149]

[0150] in, represents the clutter covariance matrix estimated by the clutter sample data in the target area; γ1,…,γ L Represents the eigenvalue, arranged from large to small, the eigenvector corresponding to the largest eigenvalue is the elevation interference phase guide: v1…v L represents the corresponding eigenvector; () T Represents a matrix transpose operation.

[0151] In an optional embodiment, the first estimation module 906 is further configured to:

[0152] The target initial velocity is obtained by using the relationship between the target velocity, the central wavelength of the frequency band and the target Doppler frequency, and adopting the target estimation method, wherein the target initial velocity is the characteristic vector with the minimum error of the Doppler offset of each frequency band.

[0153] In an optional embodiment, the compensation module 910 is further configured to:

[0154] Determine the real position area of ​​the moving target based on the target initial velocity information estimated by multi-band joint estimation;

[0155] According to the estimation result of the clutter elevation steering vector, using the clutter samples corresponding to the surrounding area of ​​the moving target real position area, estimating the first elevation steering vector of the target real position;

[0156] Calculate the elevation difference guidance vector between the real position of the target and the detection position according to the first elevation guidance vector of the real position of the target and the second elevation guidance vector of the detection position;

[0157] According to the elevation difference guidance vector, a phase compensation process is performed on the target data vector to obtain a compensated data vector.

[0158] In an optional embodiment, the third estimation module 912 is further configured to:

[0159] According to the compensated target data vector, an adaptive matched filtering algorithm is used to estimate the radial velocity of the moving target, wherein the adaptive matched filtering algorithm includes:

[0160]

[0161] in, represents the radial velocity estimation result after road compensation; represents the guidance vector containing only the target velocity; represents the compensated data vector.

[0162] In an optional embodiment, the relocation module 914 is further configured to:

[0163]

[0164] Among them, y D Indicates the position corresponding to the target detection result; R T Indicates the target slant distance; v a Indicates the flight speed of the satellite platform.

[0165] In an optional embodiment, the iteration module 916 is further configured to:

[0166] Based on the decision conditions, the moving target relocation result is subtracted from the previous estimation result to obtain the moving target relocation error, and the relocation result is judged, wherein the decision conditions include:

[0167]

[0168] Among them, v rn Indicates the target radial velocity estimation result in the current iteration; v rn-1 Represents the target radial velocity estimation result in the last iteration; y o Indicates the location of the road centerline; The elevation difference guidance vector representing the target's true position and detected position in the current iteration; The elevation difference guidance vector represents the elevation difference between the target's real position and the detected position in the previous iteration; ε1, ε2 are given constants that can be set according to the system repositioning accuracy.

[0169] The device provided by the embodiment of the present application has the following beneficial effects: 1. The present application adopts an iterative adaptive matched filtering method to realize radial velocity estimation and relocation of moving targets of space-based distributed multi-frequency multi-baseline radar. The technical means based on data estimation and compensation of target elevation guidance vector can significantly improve the accuracy of radial velocity estimation, and overcome the problem of reducing the accuracy of speed measurement and relocation due to the influence of terrain elevation coupling in the prior art; the method of alternating iteration of target elevation compensation and target positioning is adopted to solve the problem of mutual coupling between target speed measurement and positioning error and terrain elevation compensation error; the signal processing process involved in the present invention is completely based on observation data, which overcomes the problem of prior art dependence on prior information, and thus solves the problem of speed measurement and positioning error caused by inaccurate prior information. 2. The present application obtains the initial velocity of the target by means of joint estimation of target Doppler deviation between multiple frequency bands, and uses it as auxiliary information to obtain the initial value of terrain phase compensation, which can effectively weaken terrain elevation coupling, reduce the error of initial speed measurement and positioning, and accelerate the iterative convergence speed and avoid the divergence of iterative results. The present invention overcomes the problems in the prior art that when the radial velocity of the target is large, the azimuth offset of the target detection position is large and the iterative processing cannot converge, and the problem that the target speed measurement and positioning accuracy is low in complex geographical scenes with rapidly changing terrain.

[0170] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the distributed radar target speed measurement and positioning device based on iterative adaptive filtering, since it is basically similar to the distributed radar target speed measurement and positioning method embodiment based on iterative adaptive filtering, the description is relatively simple, and the relevant parts can be referred to the partial description of the distributed radar target speed measurement and positioning method embodiment based on iterative adaptive filtering.

[0171] Fig.10 This is a block diagram of a computing device provided in an embodiment of the present application. The components of the computing device 1000 include but are not limited to a memory 1010 and a processor 1020. The processor 1020 is connected to the memory 1010 via a bus 1030, and a database 1050 is used to store data.

[0172] The computing device 1000 also includes an access device 1040, which enables the computing device 1000 to communicate via one or more networks 1060. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 1040 may include one or more of any type of network interface (e.g., a network interface card (NIC)) of wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a world-wide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, and a near field communication (NFC).

[0173] In one embodiment of the present specification, the above components of the computing device 1000 and Fig.10 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Fig.10 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0174] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computing device embodiment, since it is basically similar to the distributed radar target speed measurement and positioning method embodiment based on iterative adaptive filtering, the description is relatively simple, and the relevant parts can be referred to the partial description of the distributed radar target speed measurement and positioning method embodiment based on iterative adaptive filtering.

[0175] An embodiment of the present specification also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-mentioned distributed radar target speed measurement and positioning method based on iterative adaptive filtering.

[0176] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computer-readable storage medium embodiment, since it is basically similar to the distributed radar target speed measurement and positioning method embodiment based on iterative adaptive filtering, the description is relatively simple, and the relevant parts can be referred to the partial description of the distributed radar target speed measurement and positioning method embodiment based on iterative adaptive filtering.

[0177] An embodiment of the present specification also provides a computer program, wherein when the computer program is executed in a computer, the computer is instructed to execute the steps of the above-mentioned distributed radar target speed measurement and positioning method based on iterative adaptive filtering.

[0178] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computer program embodiment, since it is basically similar to the distributed radar target speed measurement and positioning method embodiment based on iterative adaptive filtering, the description is relatively simple, and the relevant parts can be referred to the partial description of the distributed radar target speed measurement and positioning method embodiment based on iterative adaptive filtering.

[0179] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0180] It should be noted that the above is a description of a specific embodiment of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of the present specification.

[0181] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0182] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that technicians in the relevant technical field can well understand and use this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A distributed radar target speed measurement and positioning method based on iterative adaptive filtering, characterized in that: include: Distributed multi-frequency and multi-baseline radar echoes complete frequency band separation, and each frequency band echo independently completes SAR imaging; Each frequency band independently performs clutter suppression and target detection in the SAR imaging domain; According to the independent target detection results of each frequency band, the target initial speed is estimated by combining multiple frequency bands, and the target initial speed is used as the iteration initial value; According to the clutter suppression and the target detection results, estimating the clutter elevation steering vector in the target neighborhood by using an eigenvalue decomposition method for the center frequency band; According to the estimation result of the clutter elevation guidance vector, the target initial velocity is estimated by using multi-band joint estimation to assist in compensating the elevation difference of the moving target detection position, so as to obtain a compensated target data vector; estimating the radial velocity of the moving target using an adaptive matched filtering algorithm according to the compensated target data vector; repositioning the target according to the estimated result of the radial velocity; The moving target relocation error is obtained by subtracting the moving target relocation result from the previous estimation result, and the relocation result is judged; According to the moving target relocation result, the elevation difference of the moving target detection position is recompensed, and the final target radial velocity estimation and relocation result is obtained through iterative processing.

2. The method according to claim 1, characterized in that Each frequency band independently performs SAR imaging domain clutter suppression and target detection, including: In the configuration of distributed multi-frequency multi-baseline radar system, terrain interferometry phase compensation is used to eliminate the terrain dependence of clutter; According to the interferometric phase compensation results, the space-time adaptive algorithm in SAR image domain is used to suppress clutter. After clutter suppression, the constant false alarm detection method is used to detect the target.

3. The method according to claim 1, characterized in that The method of estimating the clutter elevation steering vector by using an eigenvalue decomposition method in the target neighborhood for the center frequency band according to the clutter suppression and the target detection result includes: According to the clutter suppression and the target detection results, based on a first target calculation formula, for a center frequency band, an eigenvalue decomposition method is used to estimate a clutter elevation steering vector in a target neighborhood, wherein the first target calculation formula includes: in, represents the clutter covariance matrix estimated by the clutter sample data in the target area; γ1,…,γ L Represents the eigenvalue, arranged from large to small, the eigenvector corresponding to the largest eigenvalue is the elevation interference phase guide: v1…v L Represents the corresponding eigenvector; () T Represents a matrix transpose operation.

4. The method according to claim 1, characterized in that The method of estimating the target initial speed by combining multiple frequency bands according to the independent target detection results of each frequency band, and using the target initial speed as the iteration initial value, includes: The target initial velocity is obtained by using the relationship between the target velocity, the central wavelength of the frequency band and the target Doppler frequency, and adopting the target estimation method, wherein the target initial velocity is the characteristic vector with the minimum error of the Doppler offset of each frequency band.

5. The method according to claim 1, characterized in that The method of using the target initial velocity estimated by multi-band joint estimation based on the estimation result of the clutter elevation guidance vector to assist in compensating the elevation difference of the moving target detection position to obtain the compensated target data vector includes: Determine the real position area of ​​the moving target based on the target initial velocity information estimated by multi-band joint estimation; According to the estimation result of the clutter elevation steering vector, using the clutter samples corresponding to the surrounding area of ​​the moving target real position area, estimating the first elevation steering vector of the target real position; Calculate the elevation difference guidance vector between the real position of the target and the detection position according to the first elevation guidance vector of the real position of the target and the second elevation guidance vector of the detection position; According to the elevation difference guidance vector, a phase compensation process is performed on the target data vector to obtain a compensated data vector.

6. The method according to claim 1, characterized in that The method of estimating the radial velocity of the moving target by using an adaptive matched filtering algorithm according to the compensated target data vector comprises: According to the compensated target data vector, an adaptive matched filtering algorithm is used to estimate the radial velocity of the moving target, wherein the adaptive matched filtering algorithm includes: in, represents the radial velocity estimation result after road compensation; represents the guidance vector containing only the target velocity; represents the compensated data vector.

7. The method according to claim 1, characterized in that The repositioning of the target according to the estimation result of the radial velocity comprises: Among them, y D Indicates the position corresponding to the target detection result; R T Indicates the target slant distance; v a Indicates the flight speed of the satellite platform.

8. The method according to claim 1, characterized in that The method of obtaining a moving target relocation error by subtracting the moving target relocation result from the previous estimation result and judging the relocation result comprises: Based on the decision conditions, the moving target relocation result is subtracted from the previous estimation result to obtain the moving target relocation error, and the relocation result is judged, wherein the decision conditions include: Among them, v rn Indicates the target radial velocity estimation result in the current iteration; v rn-1 Represents the target radial velocity estimation result in the last iteration; y o Indicates the location of the road centerline; The elevation difference guidance vector representing the target's true position and detected position in the current iteration; The elevation difference guidance vector represents the elevation difference between the target's real position and the detected position in the previous iteration; ε1, ε2 are given constants that can be set according to the system repositioning accuracy.

9. A distributed radar target speed measurement and positioning device based on iterative adaptive filtering, characterized in that: include: The imaging module is configured as a distributed multi-frequency multi-baseline radar echo to complete frequency band separation, and each frequency band echo independently completes SAR imaging; The detection module is configured to independently perform clutter suppression and target detection in the SAR imaging domain in each frequency band; A first estimation module is configured to estimate the target initial speed in combination with multiple frequency bands according to the independent target detection results of each frequency band, and use the target initial speed as an iteration initial value; A second estimation module is configured to estimate the clutter elevation steering vector in the target neighborhood using an eigenvalue decomposition method for a center frequency band according to the clutter suppression and the target detection results; A compensation module is configured to assist in compensating the elevation difference of the moving target detection position according to the estimation result of the clutter elevation guidance vector and using the target initial velocity estimated by multi-band joint estimation to obtain a compensated target data vector; A third estimation module is configured to estimate the radial velocity of the moving target using an adaptive matched filtering algorithm according to the compensated target data vector; a repositioning module, configured to reposition the target according to the estimation result of the radial velocity; The judgment module is configured to obtain a moving target relocation error by subtracting the moving target relocation result from the previous estimation result, and judge the relocation result; The iteration module is configured to re-compensate the elevation difference of the moving target detection position according to the moving target relocation result, and obtain the final target radial velocity estimation and relocation result through iterative processing.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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