一种利用稀疏表示的距离扩展目标检测新方法

doi:10.3969/j.issn.1001-2400.2014.03.004

Abstract

Abstract:

The problem of detecting extended targets embedded in the Gaussian noise is considered. The Sinc basis is introduced to sparsely represent the high resolution range profile (HRRP) of extended targets. Bringing the sparse representation theory into the target detection, a new method is proposed to solve the problem by the adaptive subspace detector (ASD) based on Sinc basis. Firstly, the noise power is estimated by basis pursuit (BP), then the basis pursuit de-noising (BPDN) is used to estimate the noise covariance by the residues and finally, the ASD of the first order Gaussian model realizes the extended target detection. Experimental results based on the raw data measured by the wideband radar show that the Sinc basis can sparsely represent HRRPs very well and that the proposed method can effectively realize the extended target detection.

Key words: basis pursuit de-nosing, Sinc basis, subspace detector, sparse representation

ZHANG Xiaowei;LI Ming;ZUO Lei. New detection method for extended targets using sparse representation[J].J4, 2014, 41(3): 20-25.

References

［1］徐刚, 陈倩倩, 侯育星, 等. 前视扫描SAR超分辨成像［J］. 西安电子科技大学学报, 2012, 39(5): 79-84.
Xu Gang, Chen Qianqian, Hou Yuxing, et al. Super-resolution Imaging of Forward-looking Scan SAR ［J］. Journal of Xidian University, 2012, 39(5): 79-84.
［2］盛佳恋, 张磊, 邢孟道, 等. 一种利用稀疏统计特性的超分辨ISAR成像方法［J］. 西安电子科技大学学报, 2012, 39(6): 55-60.
Sheng Jialian, Zhang Lei, Xing Mengdao, et al. Super-resolution ISAR Imaging Method with Sparse Statistics ［J］. Journal of Xidian University, 2012, 39(6): 55-60.
［3］李志鹏, 马田香, 杜兰, 等. 在雷达HRRP识别中多特征融合多类分类器设计［J］. 西安电子科技大学学报, 2013, 40(6): 111-117.
Li Zhipeng, Ma Tianxiang, Du Lan, et al. Multi-classifier Design for Feature Fusion in Radar HRRP Recognition ［J］. Journal of Xidian University, 2013, 40(6): 111-117.
［4］王鹏辉, 杜兰, 刘宏伟, 等. 雷达高分辨距离像分帧新方法［J］. 西安电子科技大学学报, 2011, 38(6): 22-29.
Wang Penghui, Du Lan, Liu Hongwei, et al. New Frame Segmentation Method for Radar HRRPs ［J］. Journal of Xidian University, 2011, 38(6): 22-29.
［5］戴奉周, 刘宏伟, 吴顺君. 一种基于顺序统计量的距离扩展目标检测器［J］. 电子与信息学报, 2009, 31(10): 2488-2492.
Dai Fengzhou, Liu Hongwei, Wu Shunjun. Order-statistic-based Detector for Range Spread Target ［J］. Journal of Electronics ＆ Information Technology, 2009, 31(10): 2488-2492.
［6］ Liu J, Zhang Z, Yang Y, et al. A CFAR Adaptive Subspace Detector for First-order or Second-order Gaussian Signals Based on a Single Observation ［J］. IEEE Transactions on Signal Processing, 2011, 59(11): 5126-5140.
［7］ Shui P, Liu H, Bao Z. Range-spread Target Detection Based on Cross Time-frequency Distribution Features of Two Adjacent Received Signals ［J］. IEEE Transactions on Signal Processing, 2009, 57(10): 3733-3745.
［8］杜兰. 雷达高分辨距离像目标识别方法研究［D］. 西安：西安电子科技大学, 2007.
［9］张晓伟, 李明, 左磊. 基于基追踪-Moore Penrose逆矩阵的稀疏信号重构［J］. 电子与信息学报, 2013, 36(2): 388-393.
Zhang Xiaowei, Li Ming, Zuo Lei. Sparse Signal Reconstruction Based on Basis Pursuit-Moore-Penrose Inverse Matrix ［J］. Journal of Electronics ＆ Information Technology, 2013, 36(2): 388-393.
［10］ Chen S, Donoho D L, Saunders M A, et al. Atomic Decomposition By Basis Pursuit ［J］. SIAM Journal of Science Computating, 1998, 20(1): 33-61.

[1]	LU Yan,LIAO Guisheng,HUANG Qingxiang. Cross-camera moving target tracking algorithm based on sparse representation [J]. Journal of Xidian University, 2021, 48(2): 197-204.
[2]	ZHAO Hui,ZHANG Le,LIU Yingli,ZHANG Jing,ZHANG Tianqi. Optimization of the low coherence and high robustness observation matrix [J]. Journal of Xidian University, 2019, 46(6): 171-178.
[3]	HE Wangpeng,HU Jie,CHEN Binqiang,LI Cheng,GUO Baolong. Sparsity-induced resonance demodulation method for blade crack detection [J]. Journal of Xidian University, 2019, 46(6): 75-80.
[4]	YANG Lei,YUE Yunze,LI Pucheng,ZHANG Tao,YANG Huan. Sparse representation of large dynamic range SAR imaging for multiple ground moving targets [J]. Journal of Xidian University, 2019, 46(5): 31-40.
[5]	WANG Hongyan,QIU Helei,PEI Tengda. Visual tracking method using discriminant dictionary learning [J]. Journal of Xidian University, 2019, 46(4): 150-158.
[6]	JIA Hairong,WANG Weimei,WANG Yan,PEI Junhua. Speech enhancement based on discriminative joint sparse dictionary alternate optimization [J]. Journal of Xidian University, 2019, 46(3): 74-81.
[7]	YU Lihong,ZHAO Jiaxiang. Design of sparse MIMO equalizers using least angle regression [J]. Journal of Xidian University, 2019, 46(1): 73-78.
[8]	WANG Chuanchuan,ZENG Yonghu,FU Weihong,WANG Liandong. Estimation algorithm for an underdetermined mixing matrix based on maximum density point searching [J]. Journal of Xidian University, 2019, 46(1): 106-111.
[9]	LI Long;LIU Zheng. Noise-robust multi-feature joint learning HRRP recognition method [J]. Journal of Xidian University, 2018, 45(4): 57-62.
[10]	GUO Xin;JIA Hairong;WANG Dong. Speech enhancement using the improved K-SVD algorithm by subspace [J]. Journal of Xidian University, 2016, 43(6): 109-115.
[11]	KONG Fanqiang;BIAN Chending;LI Yunsong;GUO Wenjun. Hyperspectral unmixing method based on the non-convex sparse and low-rank constraints [J]. Journal of Xidian University, 2016, 43(6): 116-121.
[12]	DING Jun;LIU Hongwei;CHEN Bo;WANG Yinghua. SAR image target recognition in lack of pose images [J]. Journal of Xidian University, 2016, 43(4): 5-9.
[13]	ZHAO Yonghong;ZHANG Linrang;LIU Nan;XIE Hu. DOA estimation method based on the covariance matrix sparse representation [J]. Journal of Xidian University, 2016, 43(2): 58-63+101.
[14]	WANG Yong;FENG Tangzhi;CHEN Chuchu;QIAO Qianqian;YANG Xiaoyu;WANG Guodong. Adaptive sparse representation and total variation constraint based image reconstruction [J]. J4, 2016, 43(1): 12-17+109.
[15]	XIE Hu;FENG Dazheng;YU Hongbo;YUAN Mingdong;NIE Weike. Fast space-time adaptive processing method by using the sparse representation [J]. J4, 2015, 42(5): 55-62.

New detection method for extended targets using sparse representation

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 0