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WO2019047210A1 - Procédé et système de traitement spatio-temporel adaptatif de récupération éparse basée sur les connaissances - Google Patents

Procédé et système de traitement spatio-temporel adaptatif de récupération éparse basée sur les connaissances Download PDF

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Publication number
WO2019047210A1
WO2019047210A1 PCT/CN2017/101216 CN2017101216W WO2019047210A1 WO 2019047210 A1 WO2019047210 A1 WO 2019047210A1 CN 2017101216 W CN2017101216 W CN 2017101216W WO 2019047210 A1 WO2019047210 A1 WO 2019047210A1
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Prior art keywords
clutter
angle
doppler
space
unit
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Chinese (zh)
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阳召成
汪小叶
朱轶昂
黄建军
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Shenzhen University
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Shenzhen University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00

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  • the invention belongs to the field of radar signal processing, and in particular relates to a knowledge-based sparse recovery space-time adaptive processing method and system.
  • Space-time adaptive processing is the key technology to improve the performance of airborne radar to detect moving targets, but this technology faces the challenge of limited filter training samples, and the challenge is more severe in non-uniform clutter environments.
  • the space-time adaptive processing technology has achieved certain developments, such as the proposed reduced dimension STAP (space-time adaptive processing) method, and the reduced rank STAP method. , model-based STAP method, knowledge-aided STAP method, and so on.
  • the compressed-sensing STAP method can solve the problem of insufficient training samples mentioned above (such methods usually require 4-6 training samples to obtain satisfactory output performance), and thus have attracted extensive attention from scholars at home and abroad.
  • the technical problem to be solved by the present invention is to provide a knowledge-based sparse recovery space-time adaptive processing method and system, which aims to solve the problem of performance degradation and high computational complexity due to array errors in solving the sparse recovery STAP in the prior art.
  • the present invention is implemented in this way, a knowledge-based sparse recovery space-time adaptive processing method, comprising:
  • a space-time oriented dictionary is constructed according to the airborne radar and the angle-Doppler plane, and the clutter angle-Doppler image is determined according to the space-time oriented dictionary;
  • a space-time filter is constructed according to the clutter covariance matrix, and the spurious suppression is performed by the space-time filter.
  • a plurality of clutter angle-Doppler units are distributed along the clutter ridge line on the angle-Doppler plane, and the space-time oriented dictionary is constructed according to the airborne radar and the clutter angle-Doppler unit.
  • determining the clutter angle based on the - space time oriented dictionary - the Doppler image includes:
  • the Le unit and its adjacent angle-Doppler units include:
  • the angle-Doppler unit selected by the rectangular window is a clutter angle-Doppler unit to be determined
  • the abstract matrix is constructed according to the position of the clutter angle to be determined, where the Doppler unit is located.
  • the calculating the clutter covariance matrix according to the joint estimation comprises:
  • the clutter covariance matrix is calculated according to the space time power spectrum.
  • the invention also provides a knowledge-based sparse recovery space-time adaptive processing system, comprising:
  • a dictionary building unit configured to construct a space-time guiding dictionary according to the airborne radar and the angle-Doppler plane, and determine a clutter angle-Doppler image according to the space-time guiding dictionary
  • a joint estimating unit configured to perform joint estimation on an array amplitude and phase error of the antenna array of the airborne radar and the clutter angle-Doppler image
  • a matrix calculation unit configured to calculate a clutter covariance matrix according to the joint estimate
  • a filter construction unit configured to construct a space-time filter according to the clutter covariance matrix, and perform clutter suppression by the space-time filter.
  • dictionary construction unit is specifically configured to:
  • the space-time oriented dictionary is dimension-reduced according to the abstract matrix, and the clutter angle-Doppler image is determined according to the reduced-dimensional space-time oriented dictionary.
  • dictionary construction unit is further configured to:
  • the angle-Doppler unit selected by the rectangular window is a clutter angle-Doppler unit to be determined
  • the abstract matrix is constructed according to the position of the clutter angle to be determined, where the Doppler unit is located.
  • the matrix calculation unit is configured to:
  • the clutter covariance matrix is calculated according to the space time power spectrum.
  • the present invention has the beneficial effects that the embodiment of the present invention constructs a space-time guiding dictionary according to the airborne radar and the angle-Doppler plane and determines the clutter angle-Doppler image, and the clutter angle - Amplitude and phase error of the array of Doppler and airborne radar antenna arrays are jointly estimated, the clutter covariance matrix is calculated according to the joint estimation, and the space-time filter is designed according to this, and the space-time filter is designed to perform clutter suppression. .
  • the embodiment of the invention solves the problem that the performance of the sparse recovery STAP is degraded due to the array error and the computational complexity is high, and the radar system suppression level and the target detection capability are improved.
  • FIG. 1 is a flowchart of a knowledge-based sparse recovery space-time adaptive processing method according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of selection of a clutter angle-Doppler unit according to an embodiment of the present invention
  • FIG. 5 is a schematic diagram showing the output SINR of the KA-OMP-LS-STAP under different rectangular window sizes when the prior art knowledge is precisely known, the amplitude and phase error of the array is
  • max 25%/3.5°. a Doppler frequency relationship graph different from the target;
  • FIG. 6 is a schematic diagram of the output SINR of the KA-OMP-LS-STAP under different rectangular window sizes when the prior art knowledge is precisely known, the amplitude and phase error of the array is
  • max 50%/45°. a Doppler frequency relationship graph different from the target;
  • FIG. 7 is a diagram showing the output SINR of the KA-OMP-LS-STAP is different from the target when the amplitude and phase error of the array is
  • max 25%/3.5° and the yaw angle has a measurement error according to an embodiment of the present invention.
  • FIG. 8 is a diagram showing the output SINR of the KA-OMP-LS-STAP is different from the target when the amplitude error of the array is
  • max 50%/45° and the yaw angle has measurement error according to the embodiment of the present invention. Puller frequency relationship curve;
  • FIG. 9 is a diagram showing the output SINR of the KA-OMP-LS-STAP and the target when the amplitude error of the array is
  • max 25%/3.5° and the measurement error exists in the platform speed according to the embodiment of the present invention.
  • FIG. 10 is a diagram showing the output SINR of the KA-OMP-LS-STAP and the target when the amplitude error of the array is
  • max 50%/45° and the measurement error exists in the platform speed according to the embodiment of the present invention.
  • 11 is an output SINR of a KA-OMP-LS-STAP when the amplitude error of the array is
  • max 25%/3.5° and the measurement error exists in the platform velocity and the yaw angle provided by the embodiment of the present invention.
  • FIG. 12 is a diagram showing the output SINR of the KA-OMP-LS-STAP when the amplitude error of the array is
  • max 50%/45° and the measurement error exists in the platform velocity and the yaw angle provided by the embodiment of the present invention.
  • max 50%/45° and the measurement error exists in the platform velocity and the yaw angle provided by the embodiment of the present invention.
  • FIG. 13 is a schematic diagram of output power of different algorithms along a distance unit under MountainTop data according to an embodiment of the present invention.
  • FIG. 14 is a schematic diagram of calculations of three algorithms for comparing KA-OMP-STAP, KA-OMP-LS-STAP, and OMP-LS-STAP by computer simulation experiments according to an embodiment of the present invention.
  • FIG. 15 is a schematic structural diagram of a knowledge-based sparse recovery space-time adaptive processing system according to an embodiment of the present invention.
  • FIG. 1 is a schematic diagram of a knowledge-based sparse recovery space-time adaptive processing method according to an embodiment of the present invention, including:
  • S102 Perform joint estimation on an array amplitude phase error of the antenna array of the airborne radar and the clutter angle-Doppler image.
  • the values obtained after the joint estimation are the array amplitude and phase error and the clutter angle-Doppler image.
  • the embodiment of the present invention calculates the folding coefficient of the clutter ridge by using the prior knowledge of the motion speed, the moving direction of the airborne radar, and the orientation of the antenna array, and determines the true clutter angle - the Doppler unit is at the entire angle.
  • - the distribution in the Doppler plane according to which the true clutter angle - Doppler unit and The space-time steering vector corresponding to the adjacent unit is selected from the complete space-time guiding dictionary corresponding to the entire angle-Doppler plane, thereby obtaining a reduced space-time guiding dictionary, and then performing in the reduced space-time guiding dictionary. Sparse recovery of clutter and correction of array amplitude and phase error.
  • the processing method provided by the embodiment of the present invention is simply referred to as KA-OMP-LS-STAP.
  • the antenna of a pulse-Doppler positive side view airborne radar is a uniform linear array comprising M receiving array elements, and the airborne radar transmits N pulses in a coherent processing unit.
  • the array does not have amplitude and phase errors.
  • the NM ⁇ 1 dimension free-time snapshot with no target can be expressed as:
  • x c is the space-time snapshot corresponding to the clutter
  • n is the NM ⁇ 1 dimension receiver thermal noise
  • N d M s ⁇ 1 dimension
  • ( ⁇ ) T is transpose operation
  • v d ( ⁇ ) and v s ( ⁇ ) are the time domain steering vector and the spatial domain steering vector, respectively
  • (f d,i , f s,k ) is the ith time domain grid point.
  • N s and N d are along the spatial frequency axis and time / Dopp respectively The number of grid points of the frequency axis).
  • ⁇ s [ ⁇ s,1 , ⁇ s,2 ,..., ⁇ s,M ] T is the amplitude and phase error of the antenna array, and ⁇ s,i is the amplitude and phase error corresponding to the ith array element.
  • the space-time steering vector under the amplitude and phase error of the array can be expressed as make Where I N is an identity matrix of N ⁇ N dimensions, and diag( ⁇ s ) is a diagonal matrix after diagonalization of ⁇ s , For Kronecker product, which is Hadamard product, the complete space-time guidance dictionary under array error can be expressed as ⁇ .
  • the space-free snapshot received without the target under the array error is:
  • N is a column vector of N x 1 dimension and all elements are all 1.
  • formula (2) can be expressed as:
  • Step 1 Construct a space-time oriented dictionary
  • the angle of the clutter - the Doppler element is distributed along the clutter ridge over the entire angle-Doppler plane.
  • the folding coefficient ⁇ of the clutter ridge can be calculated by a priori knowledge of the speed of the airborne radar, the direction of motion and the orientation of the antenna array:
  • f d represents the clutter Doppler frequency
  • v p is the motion velocity of the airborne radar platform
  • T r represents the pulse repetition interval
  • d is the antenna array element spacing.
  • the antenna array receives the space-time snapshot without the target, which can be expressed as:
  • Step 2 Joint Estimation of Array Amplitude and Phase Error and Clutter Angle-Doppler Image
  • the OMP algorithm based on multiple snapshot samples can be used to solve the optimization problem.
  • y l,m and x l,m are vectors respectively And the mth element in x l .
  • Step 3 Estimating the clutter covariance matrix
  • the clutter angle of the L snapshot samples - the Doppler image A and the array amplitude phase error ⁇ s (ie, After calculating the space-time power spectrum of the clutter:
  • Step 4 Design a space-time filter
  • the space-time filter weight vector is designed to:
  • the radar simulation and scene parameters involved in the simulation experiment of the embodiment of the present invention are shown in Table 1. It is assumed that 361 clutter scattering points are equally spaced on the distance ring elements of interest, and their amplitudes are subject to a Gaussian distribution. In this experimental example, the amplitude and phase errors of the array are subject to random uniform distribution, and the internal noise of the receiver obeys a Gaussian distribution, and its power is 1 for a single pulse and a single channel.
  • Figure 4 shows.
  • this experiment compares its performance with the performance of OMP-LS-STAP and the OMP based traditional power spectrum sparse recovery STAP algorithm (abbreviated as: OMP-STAP).
  • OMP-STAP traditional power spectrum sparse recovery STAP algorithm
  • the performance of the embodiment of the present invention is at least 40 dB better than that of the conventional power spectrum sparse recovery STAP, and almost reaches a small array amplitude and phase error.
  • the simulation results of FIG. 4 show that the processing method provided by the embodiment of the present invention is more robust than the OMP-LS-STAP algorithm when the amplitude error of the array is large.
  • the performance of the embodiment of the present invention is at least 25 dB better than the OMP-LS-STAP algorithm when the array amplitude error is
  • max 50%/45°. It can be seen that the embodiment of the present invention reduces the dimension of the space-time oriented dictionary by using prior knowledge, and reduces the search space of the sparse recovery algorithm, thereby improving the accuracy of joint estimation under larger array amplitude and phase errors.
  • the second experiment examines that when the prior knowledge is precisely known, the array amplitude error is
  • max 25%/3.5°,
  • max 50%/45°, the embodiment of the present invention
  • max 50%/45°
  • the embodiment of the present invention The output performance under different rectangular window sizes is shown in Figure 5 and Figure 6.
  • the third experiment examined the amplitude and phase error of the array as
  • max 25%/3.5°,
  • max 50%/45°, and the output performance of the embodiment of the present invention when the prior knowledge is inaccurate, 7 through 12, assume experimental airborne platform velocity v up measurement error and measurement error of the yaw angle ⁇ u satisfy the uniformly distributed random.
  • the experimental results show that the embodiment of the present invention can also exhibit better robust performance under a rectangular window of suitable size when the prior knowledge is inaccurate. When the deviation between the prior knowledge and the true value is larger, the rectangular window is also larger, which can ensure that the rectangular window can select all the real clutter angle-Doppler units, thereby improving the performance of the algorithm. As shown in Figs.
  • the platform motion velocity measurement error is v up ⁇ [-10, 10] m / s
  • the fourth experiment verified the effectiveness of the embodiments of the present invention using MountainTop data t38pre01v1.mat, CPI6.
  • the measured data contains 403 independent distance dimension sampling samples.
  • the clutter azimuth is 245° and the target azimuth is 275°, both of which have a Doppler frequency of 156 Hz.
  • the number of samples used to train the space-time filter is 18.
  • 6 samples adjacent to the distance unit to be detected are used as protection units.
  • the sparsity is set to 20
  • the number of alternate iterations is 20
  • the space-time oriented dictionary N d N s 4N ⁇ 4M
  • other parameters are the same as in Table 1.
  • Figure 13 shows the output power of different algorithms over a distance of 145-165 km (a distance unit with a target of 154 km).
  • Table 2 shows the difference between the output power of each algorithm at the target distance unit and the second high output power of the adjacent distance unit.
  • the results of FIG. 13 and Table 2 show that the clutter suppression performance of the processing method provided by the embodiment of the present invention is slightly better than the clutter suppression performance of the OMP-LS-STAP algorithm compared to the OMP-LS-STAP algorithm.
  • the difference between the output power at the target distance unit and the second high output power of the adjacent distance unit is larger in the embodiment of the present invention, and thus is more advantageous for subsequent constant false alarm processing.
  • the fifth experiment discusses the computational complexity of the embodiments of the invention (KA-OMP-LS-STAP) and OMP-LS-STAP. Because the two algorithms use the same algorithm for calculating the space-time filter weight vector and estimating the amplitude and phase error of the array, and the maximum number of iterations is the same in the iterative solution, the two algorithms are discussed in estimating the clutter angle-Doppler. The computational complexity of the image time is equivalent to discussing the computational complexity of the two algorithms.
  • the computational complexity of OMP-LS-STAP in estimating the clutter angle-Doppler image is O(NMN d N s ).
  • the computational complexity of KA-OMP-LS-STAP in estimating clutter angle-Doppler images and the knowledge-based OMP power spectrum sparse recovery STAP algorithm (abbreviated as KA-OMP-STAP, the algorithm uncorrected array amplitude The computational complexity of the error) Since the angle of the clutter-Doppler element is sparse in the angle-Doppler plane, there is ( ⁇ w is the size of the rectangular window), so the computational complexity of KA-OMP-LS-STAP is much smaller than the computational complexity of the OMP-LS-STAP algorithm.
  • KA-OMP-STAP does not use the LS algorithm to estimate the amplitude and phase error of the array, the computational complexity of the algorithm is smaller than that of KA-OMP-LS-STAP. Therefore, the computational relationship of these three algorithms is: KA-OMP-STAP ⁇ KA-OMP-LS-STAP ⁇ OMP-LS-STAP.
  • the proposed algorithm uses the sparse recovery algorithm and LS alternate iteration to jointly estimate the clutter power spectrum and the array amplitude and phase error.
  • the sparse recovery algorithm used by the former is OMP, while the sparse recovery algorithm adopted by the latter is LASSO.
  • OMP The computational complexity of OMP is O(NMN d N s ), and the computational complexity of LASSO is O((N d N s ) 3 ).
  • the comparison shows that the computational complexity of the OMP-LS-STAP algorithm is lower than the computational complexity of the IAD-SR-STAP algorithm.
  • the calculations of the three algorithms KA-OMP-STAP, KA-OMP-LS-STAP and OMP-LS-STAP are compared by computer simulation experiments, as shown in Fig. 14.
  • the algorithm runtime environment is MATLAB (R2014a, 8.3.0.532), Intel(R) Core(TM) i7-4790CPU@3.6GHz, 16.0GB (RAM), Windows10 (64bit).
  • Other parameters are shown in Table 1. All results were averaged over 100 Monte Carlo simulations.
  • FIG. 15 is a diagram showing a knowledge-based sparse recovery space-time adaptive processing system according to an embodiment of the present invention, including:
  • a dictionary construction unit 201 configured to construct a space-time guidance dictionary according to the airborne radar and the angle-Doppler plane, and determine a clutter angle-Doppler image according to the space-time guidance dictionary;
  • the joint estimating unit 202 is configured to perform joint estimation on an array amplitude and phase error of the antenna array of the airborne radar and the clutter angle-Doppler image;
  • a matrix calculation unit 203 configured to calculate a clutter covariance matrix according to the joint estimation
  • the filter construction unit 204 is configured to construct a space-time filter according to the clutter covariance matrix, and perform clutter suppression by the space-time filter.
  • dictionary construction unit 201 is specifically configured to:
  • the space-time oriented dictionary is dimension-reduced according to the abstract matrix, and the clutter angle-Doppler image is determined according to the reduced-dimensional space-time oriented dictionary.
  • dictionary construction unit 201 is further configured to:
  • the angle-Doppler unit selected by the rectangular window is a clutter angle-Doppler unit to be determined
  • the abstract matrix is constructed according to the position of the clutter angle to be determined, where the Doppler unit is located.
  • the matrix calculation unit 203 is configured to:
  • the clutter covariance matrix is calculated according to the space time power spectrum.
  • the embodiment of the invention firstly uses the prior knowledge to implement the reduced space-time oriented dictionary, and then adopts Orthogonal Matching Pursuit (OMP, orthogonal matching tracking algorithm) and Least Square (LS, least squares) alternate iterative algorithm to realize the clutter angle - Joint estimation of the phase error between the Doppler image and the array, and then design an adaptive space-time filter to achieve clutter suppression.
  • OMP Orthogonal Matching Pursuit
  • LS least squares
  • the embodiment of the invention can be applied to the radar clutter suppression field of the motion platform to solve the problem that the performance of the sparse recovery STAP is degraded due to the array error and the computational complexity is high, and the radar system suppression level and the target detection capability are improved.

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  • Computer Networks & Wireless Communication (AREA)
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Abstract

La présente invention est applicable au domaine du traitement de signaux radar, et l'invention concerne ainsi un procédé de traitement spatio-temporel adaptatif (STAP) de récupération éparse basée sur les connaissances, consistant à : construire un dictionnaire orienté espace-temps en fonction d'un radar aéroporté et d'un plan angulaire Doppler, et déterminer une image angulaire Doppler d'échos parasites en fonction du dictionnaire orienté espace-temps (S101) ; effectuer une estimation combinée d'une amplitude de réseau et d'une erreur de phase d'un réseau d'antennes du radar aéroporté et de l'image angulaire Doppler d'échos parasites (S102) ; calculer une matrice de covariance d'échos parasites en fonction de l'estimation combinée (S103) ; et construire un filtre spatio-temporel en fonction de la matrice de covariance d'échos parasites, et effectuer une suppression d'échos parasites au moyen du filtre spatio-temporel (S104). Le procédé décrit résout les problèmes de dégradation de la performance et de la haute complexité de calcul d'un STAP de récupération éparse provoqués par la présence d'une erreur de réseau et améliore le niveau de suppression d'échos parasites et la capacité de détection de cible du système radar.
PCT/CN2017/101216 2017-09-11 2017-09-11 Procédé et système de traitement spatio-temporel adaptatif de récupération éparse basée sur les connaissances Ceased WO2019047210A1 (fr)

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CN110764069A (zh) * 2019-11-14 2020-02-07 内蒙古工业大学 一种基于知识辅助的稀疏恢复stap色加载方法
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CN111537976B (zh) * 2020-07-01 2022-12-09 内蒙古工业大学 一种机载雷达的运动目标检测方法及装置
CN111580065A (zh) * 2020-07-06 2020-08-25 内蒙古工业大学 基于知识辅助的稀疏恢复stap方法
CN111999716A (zh) * 2020-09-02 2020-11-27 中国人民解放军海军航空大学 基于杂波先验信息的目标自适应融合检测方法
CN111999717A (zh) * 2020-09-02 2020-11-27 中国人民解放军海军航空大学 基于协方差矩阵结构统计估计的自适应融合检测方法
CN112415476A (zh) * 2020-11-13 2021-02-26 中国民航大学 一种基于稀疏贝叶斯学习的字典失配杂波空时谱估计方法
CN112415475A (zh) * 2020-11-13 2021-02-26 中国民航大学 一种基于原子范数的无网格稀疏恢复非正侧视阵stap方法
CN113219432A (zh) * 2021-05-14 2021-08-06 内蒙古工业大学 基于知识辅助和稀疏贝叶斯学习的运动目标检测方法

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