一种稀疏恢复的稳健配准补偿方法

doi:10.3969/j.issn.1001-2400.2017.05.028

Abstract

Abstract:

The loss of the degree of system freedom and mismatch of prior information based on the registration based compensation (RBC) method will result in a decline in computational performance. In order to solve above problems, a robust method named SRBC is proposed where the sparse recovery method is applied instead of the sub-aperture smoothing operation. First, the SRBC utilizes the sparse recovery to get the super resolution clutter space-time spectrum. Then the transition covariance matrix is calculated. Finally, the clutter covariance matrix is reconstructed by the Capon spectrum. Compared with the traditional RBC method, the SRBC proposed does not depend on sub-aperture smoothing operation and prior information and maintains the performance of clutter range-dependence. In addition, it is also steadier when the sensor error exists. Experimental simulation demonstrates the correctness and feasibility of this method.

Key words: range dependence, registration based compensation, sub-aperture smoothing, sparse recovery

LIU Yang;ZHANG Yongshun;LIU Hanwei;GUO Yiduo;LONG Zhenguo. Robust registration based compensation method based on sparse recovery[J].Journal of Xidian University, 2017, 44(5): 165-170+183.

References

［1］ FERTIG L B. Analytical Expressions for Space-time Adaptive Processing Performance［J］. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(1): 442-453.
［2］ KLEMM R. Principle of Space-time Adaptive Processing［M］. London: IEE, 2006.
［3］ SHEN M, MENG X, ZHANG L. Efficient Adaptive Approach for Airborne Radar Short-range Clutter Suppression［J］. IET Radar, Sonar and Navigation, 2012, 6(9): 900-904.
［4］ WEN C, WANG T, WU J X. Range-dependent Clutter Suppression Approach for Non-side-looking Airborne Radar Based on Orthogonal Waveforms［J］. IET Radar, Sonar and Navigation, 2015, 9(2): 210-220.
［5］ BEAU S, MARCOS S. Range Dependent Clutter Rejection Using Rang-recursive Space-time Adaptive Processing Algorithms［J］. Signal Processing, 2010, 90(1): 57-68.
［6］ ZHAO J, SHEN M W, ZHU D Y, et al. Improved Doppler Warping Method for Airborne Radar with Non-side Looking Array［J］. Transactions of Nanjing University of Aeronautics and Astronautics, 2013, 30(2): 169-174.
［7］冯为可，张永顺，张丹. 一种新的角度多普勒补偿方法［J］. 西安电子科技大学学报，2015, 42(6): 158-163.
FENG WEIKE, ZHANG YONGSHUN, ZHANG DAN. Novel Angle Doppler Compensation Method［J］. Journal of Xidian University, 2015, 42(6): 158-163.
［8］ LI X G. Accuracy Improved Image Registration Based on Pre-estimation and Compensation［J］. Pattern Recognition Letters, 2016, 70: 87-92.
［9］ SANCHEZ M, BOURNEMOUTH U, FRYAZINOV O, et al. Space-time Transfinite Interpolation of Volumetric Material Properties［J］. IEEE Transactions on Visualization and Computer Graphics, 2015, 21(2): 278-288.
［10］孙珂，张颢，李刚，等. 基于杂波谱稀疏恢复的空时自适应处理［J］. 电子学报, 2011, 39(6): 1389-1393.
SUN Ke, ZHANG Hao, LI Gang, et al. STAP via Sparse Recovery of Clutter Spectrum［J］. Acta Electronica Sinica, 2011, 39(6): 1389-1393.
［11］ HU N, YE Z F, XU D Y, et al. A Sparse Recovery Algorithm for DOA Estimation Using weighted Subspace Fitting［J］. Signal Processing, 2012, 92(10): 2566-2570.
［12］ YU H, LU G H, ZHANG H L. Adaptive Sparse Recovery of Moving Targets for Distributed MIMO Radar［C］//Advanced Materials Research: 933. Stafa-Zurich: Transactions Technology Publications Ltd, 2014: 450-455.
［13］王军华, 黄知涛, 周一宇, 等. 基于近似l₀范数的稳健稀疏重构算法［J］. 电子学报, 2012, 40(6): 1185-1189.
WANG Junhua, HUANG Zhitao, ZHOU Yiyu, et al. Robust Sparse Recovery Based on Approximate l₀ Norn［J］. Acta Electronica Sinica, 2012, 40(6): 1185-1189.
［14］ HYDER M M, MAHATA K. Focuss is a Convex-concave Procedure［C］//Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE , 2011: 4216-4219.
［15］ JEON B, SEOKBYEUNG O, OH S J. Fast Matching Pursuit Method with Distance Comparison［C］//Proceedings of the IEEE International Conference on Image Processing: 1. Piscataway: IEEE , 2000: 980-983.

[1]	ZHOU Xueqin,FENG Xiangchu,JING Mingli. Adaptive quasi-newton projection algorithm for sparse recovery [J]. Journal of Xidian University, 2019, 46(3): 14-19.
[2]	LI Wei;DENG Weibo;YANG Qiang;YSUO Ying;MIGLIORE Marco Donald. Diagnosis method for defective array elements based on compressive sensing [J]. Journal of Xidian University, 2018, 45(2): 160-165.
[3]	FENG Weike;ZHANG Yongshun;TONG Ningning. Registration based compensation method based on adaptive subspace tracking [J]. Journal of Xidian University, 2016, 43(3): 107-113.
[4]	FENG Weike;ZHANG Yongshun;ZHANG Dan. Novel angle Doppler compensation method [J]. J4, 2015, 42(6): 158-163.
[5]	LI Ming;LIAO Gui-sheng;YUAN Xiao-yi;WEN Jun. Analysis of the dependence of the vertical baseline bistatic airborne radar clutter spectrum on range [J]. J4, 2009, 36(6): 979-984.

Robust registration based compensation method based on sparse recovery

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 5

Metrics

Comments

Recommended 10