机载双基雷达SR-STAP杂波抑制方法

doi:10.19665/j.issn1001-2400.20241013

摘要/Abstract

摘要：

目前基于稀疏恢复的空时自适应处理方法,是通过将角度-多普勒平面细分为多个离散格点的方式来构建导向矢量字典的。然而,当这种方法应用于机载双基雷达杂波抑制时,会面临格点失配问题,从而导致杂波抑制算法性能下降。针对这个问题,提出了基于原子范数最小化技术进行机载双基雷达杂波抑制的方法,基于原子范数最小化的杂波抑制方法不用生成离散格点矩阵,而是直接在连续域上进行建模。利用杂波协方差矩阵的半正定性、块-托普利兹属性和低秩特性,结合交替方向乘子法去迭代求解原子范数最小化问题,可以准确估计出杂波子空间。然后,通过特征分解直接计算杂波的协方差矩阵,进而提高杂波抑制性能。仿真结果证明,在可用样本数量较少的情况下,所提算法由于规避了格点失配问题,与传统稀疏恢复方法相比,所提算法能够更精确地估计杂波协方差矩阵,具备更好的杂波抑制效果。

关键词: 机载双基雷达, 杂波抑制, 稀疏恢复, 原子范数最小化

Abstract:

The existing sparse-recovery-based space-time adaptive processing(SR-STAP) method typically discretizes the angular Doppler plane into a multitude of grid points to generate a guidance dictionary.However,when these methods are employed for clutter suppression in bistatic airborne radars,they would encounter the issue of grid point mismatch,which significantly impairs the algorithm performance.In response to this problem,this paper presents an innovative approach using the atomic norm minimization(ANM) for clutter suppression in bistatic airborne radars.Unlike traditional methods,the ANM operates in the continuous domain without the need to generate a discrete grid matrix.Leveraging the positive semi-definiteness,block-Toplitz prosperity and low-rank nature of the clutter covariance matrix(CCM),the alternating direction multiplier method(ADMM) is used to iteratively solve the ANM problem,leading to the accurate estimation of the clutter subspace.Subsequently,the CCM is directly computed through eigen decomposition,improving the clutter suppression performance.Simulation results indicate that the proposed algorithm circumvents the grid-mismatch problem,achieves a more precise CCM estimation,and outperforms convolutional sparse recovery methods in terms of clutter suppression performance,particularly with fewer training samples.

Key words: airborne bistatic radar, clutter suppression, sparse recovery, atomic norm minimization

中图分类号:

TN958

郭明明, 潘时龙, 曹兰英, 王祥传. 机载双基雷达SR-STAP杂波抑制方法[J]. 西安电子科技大学学报, 2025, 52(1): 117-129.

GUO Mingming, PAN Shilong, CAO Lanying, WANG Xiangchuan. Airborne bistatic radar SR-STAP clutter suppression algorithm[J]. Journal of Xidian University, 2025, 52(1): 117-129.

图/表 10

图1

图2

表1

图3

图4

图5

图6

图7

图8

表2

参考文献 23

[1]	BRENNAN L E, REED L S. Theory of Adaptive Radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 1973, AES, 9(2):237-252.
[2]	KLEMM R. Principles of Space, Time Adaptive Processing[M]. London: IET Radar,Sonar and Navigation, 2006.
[3]	REED L S, MADDETT J D, BRENNAN L E. Rapid Convergence Rate in Adaptive Arrays[J]. IEEE Transactions on Aerospace and Electronic Systems, 1974, AES, 10(6):853-863.
[4]	YANG Z C, LI X, WANG H Q, et al. Adaptive Clutter Suppression Based on Iterative Adaptive Approach for Airborne Radar[J]. Signal Processing, 2013, 93(12):3567-3577.
[5]	YANG Z, LI X, WANG H Q, et al. On Clutter Sparsity Analysis in Space-Time Adaptive Processing Airborne Radar[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(5):1214-1218.
[6]	DUAN K, WANG Z, XIE W, et al. Sparsity,Based STAP Algorithm with Multiple Measurement Vectors via Sparse Bayesian Learning Strategy for Airborne Radar[J]. IET Signal Processing, 2017, 11(5):544-553.
[7]	LIU C, WANG T, ZHANG S, et al. Clutter Suppression Based on Iterative Reweighted Methods with Multiple Measurement Vectors for Airborne Radar[J]. IET Radar Sonar Navigation, 2022, 16(9):1446-1459.
[8]	WANG Z T, XIE W C, DUAN K Q, et al. Clutter Suppression Algorithm Based on Fast Converging Sparse Bayesian Learning for Airborne Radar[J]. Signal Processing, 2017, 130:159-168.
[9]	MA Z Q, LIU Y M, MENG H D, et al. Sparse Recovery,Based Space,Time Adaptive Processing with Array Error Self,Calibration[J]. Electronics Letters, 2014, 50(13):952-954.
[10]	YANG Z, LAMARE R C, LIU W. Sparsity,Based STAP Design Based on Alternating Direction Method with Gain Phase Errors(2017)[J/OL].[2017-06-24]. https://arxiv.org/abs/1706.07975.
[11]	CUI W C, WANG T, WANG D G, et al. Robust SR, STAP Algorithms in Partly Calibrated Arrays for Airborne Radar[J]. Signal Processing, 2024, 219:109389.
[12]	WANG D, WANG T, CUI W, et al. A Clutter Suppression Algorithm via Enhanced Sparse Bayesian Learning for Airborne Radar[J]. IEEE Sensors Journal, 2023, 23(10):10900-10911.
[13]	LIU K, WANG T, HUANG W. An Efficient Sparse Recovery STAP Algorithm for Airborne Bistatic Radars Based on Atomic Selection under the Bayesian Framework[J]. Remote Sensing, 2024, 16(14):2534.
[14]	CAO J, WANG T, WANG D. Beam,Space Post,Doppler Reduced,Dimension STAP Based on Sparse Bayesian Learning[J]. Remote Sensing, 2024, 16(14):307.
[15]	ZOU B, WANG X, FENG W, et al. STAP Method Based on Sparse Recovery and Unsupervised Learning for Airborne Radar Clutter Suppression[J]. Remote Sensing, 2022, 14(14):3472.
[16]	ZHANG X, WANG T, WANG D. On Efficient Maximum Likelihood Algorithm for Clutter Suppression[J]. IEEE Signal Processing Letters, 2024, 31:1399-1403.
[17]	GUO Q, LIU L. Robust STAP of Dictionary Local Adaptive Filling and Learning for Nonstationary Clutter Suppression[J]. IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(2):1284-1298.
[18]	CHAE D H, SADEGHI P, KENNEDY R A. Effects of Basis,Mismatch in Compressive Sampling of Continuous Sinusoidal Signals[C]// 2010 2nd International Conference on Future Computer and Communication.Piscataway:IEEE, 2010:739-743.
[19]	BAI G T, TAO R, ZHAO J, et al. Parameter,Searched OMP Method for Eliminating Basis Mismatch in Space,Time Spectrum Estimation[J]. Signal Processing, 2017, 138:11-15.
[20]	CANDES E J,FERNANDEZ GRANDA C. Towards a Mathematical Theory of Super,resolution[J]. Communications on Pure and Applied Mathematics, 2014, 67(6):906-956.
[21]	TANG G, BHASKAR B N, SHAH P, et al. Compressed Sensingoff the Grid[J]. IEEE Transactions on Information Theory, 2013, 59(11):7465-7490.
[22]	FENG W K, GUO Y D, ZHANG Y S, et al. Airborne Radar Space Time Adaptive Processing Based on Atomic Norm Minimization[J]. Signal Processing, 2018, 148:31-40.
[23]	CHI Y, CHEN Y. Compressive Two,Dimensional Harmonic Retrieval via Atomic Norm Minimization[J]. IEEE Transactions on Signal Processing, 2015, 63(4):1030-1042.

参数	值
发射、接收阵元数	8
脉冲数	8
发射机位置/km	[150,0,8]
接收机位置/km	[0,0,8]
飞行速度/(m·s^-1)	140
脉冲重复频率/kHz	2
发射峰值功率/kW	40
主瓣照射坐标/km	[120,-100,0]
杂噪比(CNR)/dB	20
发射机阵列轴向与X轴夹角/(°)	0
发射机速度方向与X轴夹角/(°)	0
接收机阵列轴向与X轴夹角/(°)	0
接收机速度方向与X轴夹角/(°)	0

算法	时间/s
ANM-ADMM	13.72
ANM-CVX	1.77