基于稀疏随机阵列配置的CS-MIMO雷达感知矩阵构造

doi:10.7527/S1000-6893.2016.0020

电子与控制

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

基于稀疏随机阵列配置的CS-MIMO雷达感知矩阵构造

彭珍妮^1,2, 贲德², 张弓², 徐笛²

1. 南京航空航天大学中小型无人机先进技术工信部重点实验室, 南京 210016;
2. 南京航空航天大学雷达成像与微波光子技术教育部重点实验室, 南京 210016

收稿日期:2015-10-29 修回日期:2016-01-15 出版日期:2016-03-15 发布日期:2016-01-18
通讯作者: 彭珍妮,Tel.:025-84892892,E-mail:pengzhenni@nuaa.edu.cn E-mail:pengzhenni@nuaa.edu.cn
作者简介:彭珍妮,女,硕士,助理研究员。主要研究方向:雷达信号处理、新体制雷达。Tel:025-84892892,E-mail:pengzhenni@nuaa.edu.cn;贲德,男,教授,博士生导师,中国工程院院士。主要研究方向:雷达系统。Tel:025-84892451,E-mail:bende01@sohu.com
基金资助:
国家自然科学基金(61501233,61071163,61271327,61471191);江苏高等学校优势学科建设工程资助项目

Sensing matrix construction for CS-MIMO radar based on sparse random array

PENG Zhenni^1,2, BEN De², ZHANG Gong², XU Di²

1. Key Laboratory of Unmanned Aerial Vehicle Technology, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
2. Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Received:2015-10-29 Revised:2016-01-15 Online:2016-03-15 Published:2016-01-18
Supported by:
National Natural Science Foundation of China(61501233, 61071163, 61271327, 61471191);A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions

摘要/Abstract

摘要：

压缩感知(CS)理论中的感知矩阵在观测数据获取和信号重建过程中起关键性作用。目前,大部分研究通过引入高斯随机矩阵作为测量矩阵实现压缩观测,这类测量矩阵对硬件要求很高,工程实现困难。提出了一种基于稀疏随机阵列配置的压缩感知-多输入多输出(CS-MIMO)雷达中的感知矩阵构造方法,当MIMO雷达阵元配置为满足某种概率分布的稀疏随机阵列时,发射与接收导引矢量的Kronecker积能够起到压缩测量的作用。从理论上分析了所构造的感知矩阵的归一化互相关系数、Gram矩阵以及阵列方向图之间的内在联系,并证明了当随机阵元位置满足均匀分布时所构造的感知矩阵满足压缩感知重构条件。在这种稀疏随机阵列配置方式下,既可以避免额外引入随机测量矩阵,又能减少所需的阵元个数,从而大大降低CS-MIMO雷达系统复杂度。仿真实验表明,该方法具有较低的感知矩阵归一化互相关系数,与满阵CS-MIMO雷达相比能够在减少阵元个数的同时获得良好的重构性能,且使重构所需运算量大大降低。

关键词: 压缩感知, 多输入多输出雷达, 感知矩阵构造, 随机阵列配置, 重构条件

Abstract:

The sensing matrix of the compressive sensing(CS) theory plays an important role in data acquisition and signal recovery. Most of the previous research takes the Gaussian random matrix as the measurement matrix. However, it is hard to be implemented in physical electric circuit. A novel sensing matrix construction framework for CS-MIMO(multiple-input multiple-output) radar is proposed in this paper based on the sparse random array configuration. The elements of the linear array are placed at random with a fixed large aperture and when the positions of the random elements follow one certain probability distribution, the kronecker product of the transmitting and the receiving array steer vectors can serve as the sensing matrix. The relations between the cross correlations of the sensing matrix, the Gram matrix and the array pattern are investigated in detail. In particular, it is proved that the sensing matrix could satisfy the CS nonuniform recovery property when the random array is following the uniform distribution. Based on the sparse random array configuration, the CS-MIMO radar can not only avoid the additional random measurement matrix but also reduce the required elements. So the complexity of the CS-MIMO radar system is greatly reduced. The simulation experimental results show that the proposed method has lower cross correlations of the sensing matrix. Compared with the CS-MIMO radar with filled array, the proposed method is capable of better recovery performance with less elements, and the computation load for recovery is greatly reduced.

Key words: compressive sensing, multiple-input multiple-output radar, sensing matrix construction, random array configuration, recovery property

中图分类号:

彭珍妮, 贲德, 张弓, 徐笛. 基于稀疏随机阵列配置的CS-MIMO雷达感知矩阵构造[J]. 航空学报, 2016, 37(3): 1015-1024.

PENG Zhenni, BEN De, ZHANG Gong, XU Di. Sensing matrix construction for CS-MIMO radar based on sparse random array[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016, 37(3): 1015-1024.

参考文献

[1] ENDER J H G. A brief review of compressive sensing applied to radar[C]//Proceedings of the 14th International Radar Symposium, 2013:3-16.
[2] CANDES E J, WAKIN M B. An introduction to compressive sampling(a sensing/sampling paradigm that goes against the common knowledge in data acquisition)[J]. IEEE Signal Processing Magazine, 2008, 25(2):21-30.
[3] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.
[4] YU Y, PETROPULU A P, POOR H V. CSSF MIMO RADAR:Compressive-sensing and step-frequency based MIMO radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(2):1490-1504.
[5] 顾福飞, 张群, 管桦, 等. 基于压缩感知的MIMO-SAR运动误差补偿与成像[J]. 航空学报, 2014, 35(3):838-847. GU F F, ZHANG Q, GUAN H, et al. Motion error compensation and imaging for MIMO-SAR based on compressed sensing[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(3):838-847(in Chinese).
[6] ELAD M. Optimized projections for compressed sensing[J]. IEEE Transactions on Signal Processing, 2007, 55(12):5695-5702.
[7] CANDES E J. The restricted isometry property and its implications for compressed sensing[J]. Comptes Rendus Methematique, 2008, 346(9):589-592.
[8] PENG Z N, ZHANG G, ZHANG J D, et al. Optimized measurement matrix design using spatiotemporal chaos for CS-MIMO radar[J/OL]. Mathematical Problems in Engineering, 2014:1-8[2015-08-26]. http://dx.doi.org/10.1155/2014/916451.
[9] YU Y, PETROPULU A P, POOR H V. Measurement matrix design for compressive sensing-based MIMO radar[J]. IEEE Transactions on Signal Processing, 2011, 59(11):5338-5352.
[10] ZHANG J D, ZHU D Y, ZHANG G. Adaptive compressed sensing radar oriented toward cognitive detection in dynamic sparse target scene[J]. IEEE Transactions on Signal Processing, 2012, 60(4):1718-1729.
[11] LI G, ZHU Z H, YANG, D H, et al. On projection matrix optimization for compressive sensing systems[J]. IEEE Transactions on Signal Processing, 2013, 61(11), 2887-2898.
[12] YAN W J, WANG Q, SHEN Y. Shrinkage-based alternating projection algorithm for efficient measurement matrix construction in compressive sensing[J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63(5):1073-1084.
[13] 张劲东, 张弓, 潘汇, 等. 基于滤波器结构的压缩感知雷达感知矩阵优化[J]. 航空学报, 2013, 34(4):864-872. ZHANG J D, ZHANG G, PAN H, et al. Optimized sensing matrix design of filter structure based compressed sensing radar[J]. Acta Aeronautica et Astronautica Sinica, 2013, 34(4):864-872(in Chinese).
[14] YANG M, ZHANG G. Parameter identifiability of monostatic MIMO chaotic radar using compressed sensing[J]. Progress in Electromagnetics Research B, 2012, 44:367-382.
[15] CARIN L, LIU D H,GUO B. Coherence, compressive sensing, and random sensor arrays[J]. IEEE Antennas and Propagation Magazine, 2011, 53(4):28-39.
[16] LIU Y, WU M Y, WU S J. Fast OMP algorithm for 2D angle estimation in MIMO radar[J]. Electronics Letters, 2010, 46(6):444-445.
[17] CANDES E J, PLAN Y. A probabilistic and RIPless theory of compressed sensing[J]. IEEE Transactions on Information Theory, 2011, 57(11):7235-7254.
[18] HUGEL M, RAUHUT H, STROHMER T. Remote sensing via l₁ minimization[J]. Foundations of Computational Mathematics, 2014, 14(1):115-150.
[19] DONOHO D L, ELAD M, TEMLYAKOV V N. Stable recovery of sparse overcomplete representations in the presence of noise[J]. IEEE Transactions on InformationTheory, 2006, 52(1):6-18.
[20] LI J, STOICA P, XU L, et al. On parameter identifiability of MIMO radar[J]. IEEE Signal Processing Letters, 2007, 14(12):968-971.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于稀疏随机阵列配置的CS-MIMO雷达感知矩阵构造

Sensing matrix construction for CS-MIMO radar based on sparse random array

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	胡汉铎, 宋彦萍, 俞建阳, 刘瑶, 陈浮, 高文秀. 翼型不确定性量化中正交匹配追踪的应用[J]. 航空学报, 2023, 44(18): 128327-128327.
[2]	贾金伟, 刘利民, 韩壮志, 解辉. 基于压缩感知的抗SDIF分选射频隐身信号设计及回波信号处理[J]. 航空学报, 2023, 44(13): 327934-327934.
[3]	许敬晖, 乔百杰, 滕光蓉, 杨志勃, 陈雪峰. 基于压缩感知的叶端定时信号参数辨识方法[J]. 航空学报, 2021, 42(5): 524229-524229.
[4]	刘劲, 韩雪侠, 宁晓琳, 陈晓, 康志伟. 基于EMD-CS的脉冲星周期超快速估计[J]. 航空学报, 2020, 41(8): 623486-623486.
[5]	陈江涛, 章超, 刘骁, 赵辉, 胡星志, 吴晓军. 基于稀疏多项式混沌方法的不确定性量化分析[J]. 航空学报, 2020, 41(3): 123382-123382.
[6]	焦敬品, 李海平, 翟顺成, 何存富, 吴斌. 基于压缩感知的金属加筋板兰姆波健康监测技术[J]. 航空学报, 2019, 40(7): 422695-422695.
[7]	朱晓秀, 胡文华, 马俊涛, 郭宝锋, 薛东方. 双基地角时变下的ISAR稀疏孔径自聚焦成像[J]. 航空学报, 2018, 39(8): 322059-322059.
[8]	宁方立, 张超, 潘峰, 刘勇, 韦娟. 飞机起落架噪声源定位的压缩感知算法[J]. 航空学报, 2018, 39(5): 121810-121810.
[9]	陈春晖, 张群, 罗迎. 一种步进频率信号认知雷达波形优化设计方法[J]. 航空学报, 2016, 37(7): 2276-2285.
[10]	毕辉, 张冰尘, 洪文. 基于RIPless理论的层析SAR成像航迹分布优化方法[J]. 航空学报, 2016, 37(2): 680-687.
[11]	倪嘉成, 张群, 顾福飞, 孙莉, 霍文俊. 基于马尔科夫链的单站SAR海面场景宽幅高分成像算法[J]. 航空学报, 2016, 37(12): 3793-3802.
[12]	冯燕, 王忠良, 王丽. 一种高光谱图像的双压缩感知模型[J]. 航空学报, 2015, 36(9): 3041-3049.
[13]	顾福飞, 张群, 娄昊, 杨秋, 陈一畅. 一种孔径和频率二维稀疏的步进频SAR成像方法[J]. 航空学报, 2015, 36(4): 1221-1229.
[14]	方标, 黄高明, 高俊. LFM宽带雷达信号的盲压缩感知模型[J]. 航空学报, 2014, 35(8): 2261-2270.
[15]	温晓杨, 匡纲要, 胡杰民, 占荣辉, 张军. 基于实波束扫描的相控阵雷达前视成像[J]. 航空学报, 2014, 35(7): 1977-1991.