基于滤波器结构的压缩感知雷达感知矩阵优化

doi:10.7527/S1000-6893.2013.0147

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Optimized Sensing Matrix Design of Filter Structure Based Compressed Sensing Radar

ZHANG Jindong, ZHANG Gong, PAN Hui, BEN De

College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Received:2012-03-15 Revised:2012-12-10 Online:2013-04-25 Published:2013-04-23
Supported by:
National Natural Science Foundation of China (61071163, 61201367, 61071164, 61271327); Natural Science Foundation of Jiangsu Province (BK2012382); China Postdoctoral Science Foundation (20100481143); Jiangsu Planned Projects for Postdoctoral Research Funds (1109093C); Fundamental Research Funds for the Central Universities (NS2012020); A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions *Corresponding author. Tel.: 025-84892410 E-mail: zhangjd@nuaa.edu.cn

Abstract

Abstract:

The sparse scene recovery performance of a compressed sensing radar (CSR) requires that the cross correlations between the atoms of the sensing matrix be as small as possible. Based on this thought, a CSR optimal sensing matrix design system is proposed. According to the information of the radar system task and target scene, it can optimize the transmitted waveform and measurement matrix adaptively for the purpose of reducing the coherence of the sensing matrix to improve the system performance. The algorithms for optimizing the transmitted waveform and measurement matrix separately and jointly are presented. Simulation results demonstrate that the proposed methods can effectively improve scene recovery accuracy.

Key words: compressed sensing radar, sensing matrix optimization, waveform optimization, measurement matrix optimization, filter structure

CLC Number:

ZHANG Jindong, ZHANG Gong, PAN Hui, BEN De. Optimized Sensing Matrix Design of Filter Structure Based Compressed Sensing Radar[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, doi: 10.7527/S1000-6893.2013.0147.

References

[1] Candes E J, Wakin M B. An introduction to compressive sampling. IEEE Signal Processing Magazine, 2008, 25(2): 21-30.

[2] Baraniuk R, Steeghs P. Compressive radar imaging. IEEE Radar Conference, 2007: 128-133.

[3] Candes E. The restricted isometry property and its implications for compressed sensing. Comptes Rendus Methematique, 2008, 346(9): 589-592.

[4] Tropp J A. Greed is good: algorithmic results for sparse approximation. IEEE Transactions on Information Theory, 2004, 50(10): 2231-2242.

[5] Donoho D L, Elad M, Temlyakov V N. Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Transactions on Information Theory, 2006, 52(1): 6-18.

[6] Ji S H, Xue Y, Carin L. Bayesian compressive sensing. IEEE Transactions on Signal Processing, 2008, 56(6): 2346-2356.

[7] Duarte-Carvajalino J M, Sapiro G. Learning to sense spares signals: simultaneous sensing matrix and sparsifying dictionary optimization. IEEE Transactions on Image Processing, 2009, 18(7): 1395-1408.

[8] Abolghasemi V, Saideh F, Bahador M. On optimization of the measurement matrix for compressive sensing. 18th European Signal Processing Conference, 2010: 23-27.

[9] Zhang J D, Zhu D Y, Zhang G. Adaptive compressed sensing radar oriented towards cognitive detection in dynamic sparse target scene. IEEE Transactions on Signal Processing, 2012, 60(4): 1718-1729.

[10] Tropp J A, Wakin M B. Random filters for compressive sampling and reconstruction. IEEE International Conference on Acoustics, Speech and Signal Processing, 2006: 872-875.

[11] Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAM Review, 2001, 1(43): 129-159.

[12] Candes E, Romberg J, Terence T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.

[13] Tropp J A, Gilbert A C. Signal recovery from partial information by orthogonal matching pursuit. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666.

[14] Liu J Y, Zhu J B, Yan F X, et al. Design of remote sensing imaging system based on compressive sensing. Systems Engineering and Electronics, 2010,32(8):1618-1623. (in Chinese) 刘吉英, 朱炬波, 严奉霞, 等. 基于压缩感知理论的稀疏遥感成像系统设计. 系统工程与电子技术, 2010, 32(8):1618-1623.

[15] Peter S, Hao H, Jian L. New algorithms for designing unimodular sequences with good correlation properties. IEEE Signal Processing Letters, 2007, 17(3): 253-256.

[16] Stoica P, Li J, Zhu X. Waveform synthesis for diversity-based transmit beampattern design. IEEE Transactions on Signal Processing, 2008, 56(6): 2593-2598.

Optimized Sensing Matrix Design of Filter Structure Based Compressed Sensing Radar

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