基于压缩感知的CFAR目标检测算法

马俊虎; 刘长远; 甘露

doi:10.11999/JEIT170382

基于压缩感知的CFAR目标检测算法

doi: 10.11999/JEIT170382

基金项目:

国家自然科学基金委员会-中国工程物理研究院NSAF联合基金(U1530126)

计量
- 文章访问数: 1709
- HTML全文浏览量: 248
- PDF下载量: 358
- 被引次数: 0
出版历程
- 收稿日期: 2017-04-26
- 修回日期: 2017-07-10
- 刊出日期: 2017-12-19

CFAR Target Detection Algorithm Based on Compressive Sensing

Funds:

The National Natural Science Foundation of China-China Academy of Engineering Physics Joint Foundation (NSAF) (U1530126)

摘要

摘要: 该文提出一种基于压缩感知(Compressive Sensing, CS)的恒虚警率(Constant False Alarm Rate, CFAR)目标检测算法，首先分析了目标在距离单元上具有稀疏特性，并构造了目标回波的稀疏字典，设计特定的测量矩阵以及基于CS的CFAR检测结构，然后实现了对回波信号的压缩测量和CFAR检测，无需对回波信号重构。该文提出的算法具有很好的降噪性能并提高了检测效率，可以对低信噪比、低信杂比信号成功检测。仿真结果表明：当信噪比为-14 dB，信杂比为-10 dB时，该算法与传统匹配滤波检测算法相比，减少了一半数据运算量，性能明显优于压缩匹配滤波检测算法。
- 目标检测 /
- 恒虚警率 /
- 压缩感知 /
- 测量矩阵
Abstract: A new Constant False Alarm Rate (CFAR) target detection algorithm is proposed based on Compressive Sensing (CS). Firstly, the sparsity of target in the distance dimension is analyzed and the sparse dictionary is constructed for the echo signal. Secondly, a certain measurement matrix and CFAR detection structure are designed based on CS. The proposed detector can detect sparse signals directly with high accuracy without any signal reconstruction. The proposed algorithm has a good noise reduction performance, which can detect low SNR and low Signal-to-Interference Ratio (SIR) signals successfully. Finally, computer simulation results verify that when SNR is equal to -14 dB and SIR is equal to -10 dB, the proposed detector can reduce the half measurements via compared with classical Matched Filter (MF) algorithm. Whats more, the performance of the proposed detector is better than CS MF algorithm.
- Target detection /
- CFAR /
- Compressive Sensing (CS) /
- Measurement matrix

HTML全文

参考文献(28)

LI Ying, ZHANG Gong, TAO Yu, et al. Target detection in compressive sensing based on step frequency radar[J]. Modern Radar, 2015, 37(9): 22-25. doi: 10.16592/j.cnki.10047859.

李莹, 张弓, 陶宇, 等. 基于压缩感知的步进频雷达目标检测算法[J]. 现代雷达, 2015, 37(9): 22-25. doi: 10.16592/j.cnki. 10047859.

DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109/ TIT.2006.871582.

CANDES E J and WAKIN M B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2): 2130. doi: 10.1109/MSP.2007.914731.

吴建宇, 徐海东, 王珏. 基于过完备字典稀疏表示的多通道脑电信号压缩感知联合重构[J]. 电子与信息学报, 2016, 38(7): 1666-1673. doi: 10.11999/JEIT151079.

WU Jianyu, XU Haidong, and WANG Jue. A new joint reconstruction algorithm of compressed sensing for multichannel EEG signals based on over-complete dictionary approach[J]. Journal of Electronic Information Technology, 2016, 38(7): 1666-1673. doi: 10.11999/JEIT151079.

岑翼刚, 岑丽辉. 基于峰值变换的信号稀疏表示及重建[J]. 电子与信息学报, 2011, 33(2): 326-331. doi: 10.3724/SP.J. 1146.2010.00305.

CEN Yigang and CEN Lihui. Sparse representation and reconstruction of signals based on the peak transform[J]. Journal of Electronic Information Technology, 2011, 33(2): 326-331. doi: 10.3724/SP.J.1146.2010.00305.

ENDER J H G. On compressive sensing applied to radar[J]. Signal Processing, 2010, 90(5): 1402-1414. doi: 10.1016./j. sigpro.2009.11.009.

王伟伟, 廖桂生, 朱圣棋, 等. 一种基于压缩感知的地面运动目标检测方法[J]. 电子与信息学报, 2012, 34(8): 1872-1878. doi: 10.3724/SP.J.1146.2011.01285.

WANG Weiwei, LIAO Guisheng, ZHU Shengqi, et al. A ground moving target indication method based on compressive sensing[J]. Journal of Electronic Information Technology, 2012, 34(8): 1872-1878. doi: 10.3724/SP.J.1146. 2011.01285.

赵瑞珍, 王若乾, 张凤珍, 等. 分块的有序范德蒙矩阵作为压缩感知测量矩阵的研究[J]. 电子与信息学报, 2015, 37(6): 1317-1322. doi: 10.11999/JEIT140860.

ZHAO Ruizhen, WANG Ruoqian, ZHANG Fengzhen, et al. Research on the blocked ordered vandermonde matrix used as measurement matrix for compressed sensing[J]. Journal of Electronic Information Technology, 2015, 37(6): 1317-1322. doi: 10.11999/JEIT140860.

王明宇. 复杂环境下雷达CFAR检测与分布式雷达CFAR检测研究[D]. [博士论文], 西北工业大学, 2001: 20-28.

WANG Mingyu. Radar CFAR detection and distributed radar CFAR detection under complicated environments[D]. [Ph.D. dissertation], Northwestern Polytechnical University, 2001: 20-28.

RAGHAVAN R S. Analysis of CA-CFAR processors for linear-law detection[J]. IEEE Transactions on Aerospace Electronic Systems, 1992, 28(3): 661-665. doi: 10.1109/7. 256288.

ROHLING H. Radar CFAR threshold in clutter and multiple target situation[J]. IEEE Transactions on Aerospace Electronic Systems, 1983, 19(4): 608-621. doi: 10.1109/TAES. 1983.309350.

胡勤振, 苏洪涛, 周生华, 等. 多基地雷达中双门限CFAR检测算法[J]. 电子与信息学报, 2016, 38(10): 2430-2436. doi: 10.11999/JEIT151163.

HU Qinzhen, SU Hongtao, ZHOU Shenghua, et al. Double threshold CFAR detection for multisite radar[J]. Journal of Electronic Information Technology, 2016, 38(10): 2430-2436. doi: 10.11999/JEIT151163.

赵兴刚, 王首勇. 一种基于KL分离度的改进矩阵CFAR检测方法[J]. 电子与信息学报, 2016, 38(4): 934-940. doi: 10.11999 /JEIT150711.

ZHAO Xinggang and WANG Shouyong. An inproved matrix CFAR detection method base on KL divergence[J]. Journal of Electronic Information Technology. 2016, 38(4): 934-940. doi: 10.11999/JEIT150711.

ANITORI L, ROSSUM W V, OTTEN M, et al. Compressive sensing radar: Simulations and experiments for target detection[C]. Signal Processing Conference, Marrakech, Morocco, 2014: 1-5.

ANITORI L, ROSSUM W V, OTTEN M, et al. Compressive CFAR radar processing[C]. Compressive CFAR Radar Processing Tno, Repository, Bremen, Germany, 2013: 57-60.

RAZAVI A, VALKAMA M, and CABRIC D. Compressive detection of random subspace signals[J]. IEEE Transactions on Signal Processing, 2016, 64(16): 4166-4179. doi: 10.1109/ TSP.2016.256.0132.

HARIRI A and BABAIE M. Compressive detection of sparse signals in additive white gaussian noise without signal reconstruction[J]. Signal Processing, 2016, 131: 376-385. doi: 10.1016/j.sigpro.2016.08.020.

理查兹, 邢孟道, 王彤, 等. 雷达信号处理基础[M]. 北京: 电子工业出版社, 2008: 117-147.

MARK A. RICHARDS, XING Mengdao, WANG Tong, et al. Fundamentals of Radar Signal Processing[M]. Beijing: Publishing House of Electronics Industry, 2008: 117-147.

DAVENPORT M A, WAKIN M B, and BARANIUK R G. The compressive matched filter[R]. Rice University, Technical Report TREE-0610, 2006.

施引文献

资源附件(0)

访问统计