基于压缩协作表示的辐射源识别算法
收稿日期: 2015-07-13
修回日期: 2015-11-19
网络出版日期: 2015-12-07
基金资助
国家“863”计划（2014AAXXXX061)
Emitter recognition algorithm based on compressively collaborative representation
Received date: 2015-07-13
Revised date: 2015-11-19
Online published: 2015-12-07
Supported by
National High-tech Research and Development Program of China (2014AAXXXX061)
周志文 , 黄高明 , 高俊 . 基于压缩协作表示的辐射源识别算法[J]. 航空学报, 2016 , 37(7) : 2251 -2258 . DOI: 10.7527/S1000-6893.2015.0313
Aimed at performance decline of traditional emitter recognition algorithms under the low signal-to-noise ratio (SNR) conditions, an algorithm based on compressively collaborative representation is proposed, which is described from feature extraction and classifier design. The time-domain emitter signal is transformed into a 2-dimensional image through time-frequency analysis, and then a high-dimensional feature vector is extracted with image processing methods. After random compression, the features are input into the proposed classifiers to obtain recognition results. Furthermore, the non-negative constraint is posed to the collaborative representation coefficients, which makes the proposed algorithm more suitable for the practical applications. The simulation results verify the validity of the proposed algorithm and show that its robustness to noise is better when SNR is low and it can be easily realized in engineering due to the low computation burden.
[1] LUNDÈN J, KOIVUNEN V. Automatic radar waveform recognition[J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(1):124-136.
[2] LUND B B, COHEN L, CHEN V, et al. Time-frequency approach to radar detection, imaging, and classification[J]. IET Signal Processing, 2010, 4(4):325-328.
[3] ZENG D, ZENG X, LU G, et al. Automatic modulation classification of radar signals using the generalized time-frequency representation of Zhao, Atlas and Marks[J]. IET Radar, Sonar and Navigation, 2010, 5(4):507-516.
[4] WRIGHT J, YANG A Y, GANESH A, et al. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2):210-227.
[5] MA J, HUANG G M, ZUO W, et al. Robust radar waveform recognition algorithm based on random projections and sparse classification[J]. IET Radar, Sonar and Navigation, 2014, 8(4):290-296.
[6] ZHANG L, YANG M, FENG X C. Sparse representation or collaborative representation:Which helps face recognition?[C]//International Conference on Computer Vision, 2011:471-478.
[7] CHI Y J, PORIKLI F. Classification and boosting with multiple collaborative representations[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(8):1519-1531.
[8] JIA S, SHEN L L, LI Q Q. Gabor feature-based collaborative representation for hyperspectral imagery classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(2):1118-1129.
[9] LI W, DU Q, ZHANG F, et al. Collaborative-representation-based nearest neighbor classifier for hyperspectral imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(2):389-393.
[10] 龙泓琳, 皮亦鸣, 曹宗杰. 基于非负矩阵分解的SAR图像目标识别[J]. 电子学报, 2010, 38(6):1425-1429. LONG H L, PI Y M, CAO Z J. Non-negative matrix factorization for target recognition[J]. Acta Electronica Sinica, 2010, 38(6):1425-1429(in Chinese).
[11] 丁军, 刘宏伟, 王英华. 基于非负稀疏表示的SAR图像目标识别方法[J]. 电子与信息学报, 2014, 36(9):2194-2220. DING J, LIU H W, WANG Y H. SAR image target recognition based on non-negative sparse representation[J]. Journal of Electronics and Information Technology, 2014, 36(9):2194-2220(in Chinese).
[12] NADAV L, ELI M. Radar signals[M]. New York:John Wiley and Sons Inc., 2004:53-61.
[13] 张国柱. 雷达辐射源识别技术研究[D]. 长沙:国防科技大学, 2005:33-36. ZHANG G Z. Research on emitter identification[D]. Changsha:National University of Defense Technology, 2005:33-36(in Chinese).
[14] 邹兴文, 张葛祥, 李明, 等. 一种雷达辐射源信号分类新方法[J]. 数据采集与处理, 2009, 24(4):487-492. ZOU X W, ZHANG G X, LI M, et al. Novel method for classifying radar emitter signals[J]. Journal of Data Acquisition and Processing, 2009, 24(4):487-492(in Chinese).
[15] MAJUMDAR A, WARD R K. Robust classifiers for data reduced via random projections[J]. IEEE Transactions on Systems, Man, and Cybernetics, 2010, 40(5):1359-1371.
[16] LIU Z H, ZHAO X H, HUANG T, et al. Enhanced collaborative representation based classification[C]//International Conference on Information and Automation, 2014:447-450.
[17] VO N, MORAN B, CHALLA S. Nonnegative-least-square classifier for face recognition[J]. Lecture Notes in Computer Science, 2009, 5553:449-456.
[18] 张贤达. 矩阵分析与应用[M]. 北京:清华大学出版社, 2013:330-335. ZHANG X D. Matrix analysis and applications[M]. Beijing:Tsinghua University Press, 2013:330-335(in Chinese).
[19] BINGHAM E, MANNILA H. Random projection in dimensionality reduction:Applications to image and text data[C]//International Conference on KDD, 2001:483-488.
[20] TROPP J A, GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12):4655-4666.
[21] KOH K, KIM S J, BOYD S. L1LS:Simple matlab solver for l1-regularized least squares problems[EB/OL]. (2007-08)[2015-09-20]. http://www.stanford.edu/~boyd/l1_ls.
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