雷达高分辨率紧凑感知矩阵追踪算法

刘静; 盛明星; 宋大伟; 尚社; 韩崇昭

doi:10.11999/JEIT151135

雷达高分辨率紧凑感知矩阵追踪算法

doi: 10.11999/JEIT151135

1.
(西安交通大学电信学院西安 710049) ②(空间微波技术国家级重点实验室西安 710000)

基金项目:

CAST创新基金(J20141110)，国家自然科学基金(61573276)，国家973计划(2013CB329405)

计量
- 文章访问数: 1332
- HTML全文浏览量: 176
- PDF下载量: 386
- 被引次数: 11
出版历程
- 收稿日期: 2015-10-10
- 修回日期: 2016-04-22
- 刊出日期: 2016-08-19

Compact Sensing Matrix Pursuit Algorithm for Radars with High Resolution

1.
(School of Electronics and Information Engineering, Xi&rsquo
2.
(National Key Laboratory of Science and Technology on Space Microwave, Xi&rsquo

Funds:

The Innovation Foundation of CAST (J20141110), The National Natural Science Foundation of China (61573276), The National 973 Program of China (2013CB329405)

摘要

摘要: 针对压缩感知雷达的感知矩阵相干系数随分辨率增加而增大以致不能以大概率对稀疏向量进行完美重构的问题，直接基于原始感知矩阵，提出紧凑感知矩阵追踪(CSMP)算法。该文将CSMP算法应用于十字阵雷达的2维波达方向(DOA)估计并进行了计算机仿真。仿真结果表明与多信号分类(MUSIC)算法，子空间追踪(SP)算法，基追踪(BP)算法和稀疏贝叶斯学习(SBL)算法相比，基于CSMP算法的DOA估计分辨率得到了较大提高。
- 压缩感知雷达系统 /
- 高分辨率 /
- 高相关性 /
- 紧凑感知矩阵追踪算法
Abstract: In this paper, a novel algorithm named Compact Sensing Matrix Pursuit (CSMP) is proposed to deal with the high coherence problem encountered in the compressed sensing based radar system with high resolution. The CSMP algorithm is applied to the two dimensional Direction Of Arrival (DOA) estimation of cross-array. The simulation results show that the resolution can be increased largely compared with the MUltiple SIgnal Classification (MUSIC) algorithm, Subspace Pursuit (SP), Basis Pursuit (BP), and the Sparse Bayesian Learning (SBL) algorithms.
- Compressive sensing based radar system /
- High resolution /
- High coherence /
- Compact Sensing Matrix Pursuit (CSMP) algorithm

HTML全文

参考文献(20)

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

ZEINALKHANI C and BANIHASHEMI A H. Iterative reweighted l2/l1 recovery algorithms for compressed sensing of block sparse signals[J]. IEEE Transactions on Signal Processing, 2015, 63(17): 4516-4531. doi: 10.1109/TSP.2015. 2441032.

BARANIUK R and STEEGHS P. Compressive radar imaging[C]. IEEE Radar Conference, Boston, 2007: 128-133. doi: 10.1109/RADAR.2007.374203.

BAR-ILAN O and ELDAR Y C. Sub-Nyquist radar via doppler focusing[J]. IEEE Transactions on Signal Processing, 2014, 62(7): 1796-1811. doi: 10.1109/TSP.2014.2304917.

LI Hongtao, WANG Chaoyu, WANG Ke, et al. High resolution range profile of compressive sensing radar with low computational complexity[J]. IET Radar, Sonar and Navigation, 2015, 9(8): 984-990. doi: 10.1049/iet-rsn.2014. 0454.

BOURGAIN J, DILWORTH S, FORD K, et al. Explicit constructions of RIP matrices and related problems[J]. Duke Mathematical Journal, 2011, 159(1): 145-185. doi: 10.1215/ 00127094-1384809.

CHEN C and VAIDYANATHAN P. Compressed sensing in MIMO radar[C]. Asilomar Conference on Signal, Systems and Computers, Piscataway, 2008: 41-44.

SONG Xiaofeng, ZHOU Shengli, and WILLETT P. The role of the ambiguity function in compressed sensing radar[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, Dallas, 2010: 2758-2761. doi: 10.1109/ ICASSP.2010.5496221.

王超宇, 梅湄, 朱晓华, 等. 一种稳健的盲稀疏度压缩感知雷达目标参数估计方法[J]. 电子与信息学报, 2014, 36(4): 960-966. doi: 10.3724/SP.J.1146.2013.01007.

WANG Chaoyu, MEI Mei, ZHU Xiaohua, et al. A robust

blind sparsity target parameter estimation algorithm for compressive sensing radar[J]. Journal of Electronics Information Technology, 2014, 36(4): 960-966. doi: 10.3724/ SP.J.1146.2013.01007.

KIM Y G and LEE M J. Scheduling multi-channel and multi-timeslot in time constrained wireless sensor networks via simulated annealing and particle swarm optimization[J]. IEEE Communications Magazine, 2014, 52(1): 122-129.

SCHMIDT R O. Multiple emitter location and signal parameter estimation[J]. IEEE Transactions on Antennas and Propagation, 1986, 34(3): 276-280. doi: 10.1109/TAP. 1986.1143830.

DAI W and MILENKOVIC O. Subspace pursuit for compressive sensing signal reconstruction[J]. IEEE Transactions on Information Theory, 2009, 55(5): 2230-2249. doi: 10.1109/TIT.2009.2016006

ZHU Hao, GEERT L, and GEORGIOS G. Sparsity-cognizant total least-squares for perturbed compressive sampling[J]. IEEE Transactions on Signal Processing, 2011, 59(5): 2002-2016. doi: 10.1109/TSP.2011.2109956.

JI Shihao, XUE Ya, and CARIN L. Bayesian compressive sensing[J]. IEEE Transactions on Signal Processing, 2008, 56(6): 2346-2356. doi: 10.1109/TSP.2007.914345.

EVERITT B, LANDAU S, and LEESE M. Cluster Analysis[M]. London: Edward Arnold, 2001: 121-134.

DONOHO D L, ELAD M, and TEMLYAKOV V N. Stable recovery of sparse overcomplete representations in the presence of noise[J]. IEEE Transactions on Information Theory, 2006, 51(1): 6-18. doi: 10.1109/TIT.2005.860430.

林波, 张增辉, 朱炬波. 基于压缩感知的DOA估计稀疏化模型与性能分析[J]. 电子与信息学报, 2014, 36(3): 589-594. doi: 10.3724/SP.J.1146.2013.00149.

LIN Bo, ZHANG Zenghui, and ZHU Jubo. Sparsity model and performance analysis of DOA estimation with compressive sensing[J]. Journal of Electronics Information Technology, 2014, 36(3): 589-594. doi: 10.3724/SP.J.1146. 2013.00149.

施引文献

期刊类型引用(7)

1.	刘亚春，王祎鸣，杨俊钢. 基于频谱细化和二阶同步压缩变换的船载地波雷达目标检测方法. 海洋科学进展. 2024(01): 116-125 . 百度学术
2.	李发瑞，纪永刚，任继红，程啸宇，王心玲. 基于SET-CNN的紧凑型地波雷达弱目标检测方法. 海洋科学进展. 2023(04): 753-764 . 百度学术
3.	李会勇，薛长飘，陈卓，胡进峰，姚冯. 基于Hankel矩阵分解的天波超视距雷达机动目标检测算法. 电子与信息学报. 2018(03): 541-547 . 本站查看
4.	李庆忠，周祥振，黎明，牛炯. 基于时频分析的海杂波背景下舰船目标检测. 计算机应用研究. 2018(01): 52-55+61 . 百度学术
5.	张冰瑞，雷志勇，周海峰，周毅. 基于HHT的高频雷达机动目标参数估计. 系统工程与电子技术. 2018(03): 546-551 . 百度学术
6.	刘子威，苏洪涛，胡勤振. 天波超视距雷达中瞬态干扰定位方法研究. 电子与信息学报. 2016(10): 2482-2487 . 本站查看
7.	胡进峰，薛长飘，李会勇，谢菊兰. 基于最大似然法的天波超视距雷达相位解污染算法. 电子与信息学报. 2016(12): 3197-3204 . 本站查看