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

ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2016, Vol. 37 ›› Issue (3): 1015-1024.doi: 10.7527/S1000-6893.2016.0020

• Electronics and Control • Previous Articles Next Articles

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

Abstract

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

CLC Number:

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.

References

[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.

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

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