[1]TANG Lin,JIAO Shuhong.Compressed sensing radar imaging using approximate message passing[J].CAAI Transactions on Intelligent Systems,2015,10(4):592-598.[doi:10.3969/j.issn.1673-4785.201411025]
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Compressed sensing radar imaging using approximate message passing

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 4 Page number: 592-598 Column: 学术论文—智能系统 Public date: 2015-08-25
Title:
Compressed sensing radar imaging using approximate message passing
Author(s):
TANG Lin;  JIAO Shuhong
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
radar imaging; compressed sensing; approximate observation model; approximate message passing; belief propagation
CLC:
TN951
DOI:
10.3969/j.issn.1673-4785.201411025
Abstract:
In order to solve the great computational complexity in compressed sensing radar imaging due to a large observation matrix, an approximate observation model based approximate message passing algorithm is proposed in this paper. It uses inverse chirp scaling operator to approximate the large observation matrix in compressed sensing radar imaging, thereby effectively reducing the computational complexity by the current mature decoupling technology, and in the meantime, the approximate message passing is used to improve convergence rate. The theoretical analysis and simulation show that compared to the currently used compressed sensing radar imaging methods, the proposed method exhibits higher convergence rate while suffers same computational complexity in each iteration. It realizes compressed sensing radar imaging of non-complete observation data.
References:
[1] KIROLOS S, LASKA J, WAKIN M, et al. Analog-to-information conversion via random demodulation[C]// 2006 IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software. Richardson, USA: IEEE, 2006: 71-74.
[2] SALZMAN J, AKAMINE D, LEFEVRE R, et al. Interrupted synthetic aperture radar (SAR)[J]. IEEE Aerospace and Electronic Systems Magazine, 2002, 17(5): 33-39.
[3] RILLING G, DU C, DAVIES M, et al. Processing SAR data with gaps in the aperture: a compressed sensing perspective[C]// Proceedings of International Conference on Synthetic Aperture Sonar and Synthetic Aperture Radar. Lerici, Italy 2010: 62-67.
[4] DONOHO D. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[5] 谢晓春. 压缩感知理论在雷达成像中的应用研究[D]. 北京: 中国科学院研究生院, 2010: 100-102.XIE Xiaochun. Study on the application of Compressive Sensing in radar imaging[D]. Beijing: Chinese Academy of Sciences, 2010: 100-102.
[6] 全英汇. 稀疏信号处理在雷达检测和成像中的应用研究[D]. 西安: 西安电子科技大学, 2012: 46-49.QUAN Yinghui. Study on sparse signal processing for radar detection and imaging application[D]. Xi’an: Xidian University, 2012: 46-49.
[7] POTTER L C, ERTIN E, PARKER J T, et al. Sparsity and compressed sensing in radar imaging[J]. Proceedings of the IEEE, 2010, 98(6): 1006-1020.
[8] TELLO A M, LOPEZ-DEKKER P, MALLORQUI J J. A novel strategy for radar imaging based on compressive sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(12): 4285-4295.
[9] PATEL V M, EASLEY G R, HEALY JR D M, et al. Compressed synthetic aperture radar[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 244-254.
[10] ZHANG B C, JIANG H, Hong W, et al. Synthetic aperture radar imaging of sparse targets via compressed sensing[C]// 2010 8th European Conference on Synthetic Aperture Radar (EUSAR). Aachen, Germany: VDE, 2010: 1-4.
[11] DONG X, ZHANG Y H. A novel compressive sensing algorithm for SAR imaging[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(2): 708-720.
[12] FANG J, XU Z B, ZHANG B C, et al. Fast compressed sensing sar imaging based on approximated observation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(1): 352-363.
[13] MALEKI A. Approximate message passing algorithms for compressed sensing[D]Stanford, USA: Stanford University, 2011: 103-115.
[14] HIRABAYASHI A, SUGIMOTO J, MIMURA K. Approximate message passing algorithm for complex separable compressed imaging[C]// 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). Kaohsiung, China: IEEE, 2013: 1-5.
[15] MALEKI A, ANITORI L, YANG Z, et al. asymptotic analysis of complex Lasso via complex approximate message passing (Camp)[J]. IEEE Transactions on Information Theory, 2013, 59(7): 4290-4308.
[16] RANEY R K, RUNGE H, BAMLER R, et al. Precision SAR processing using chirp scaling[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(4): 786-799.
[17] SEEGER M W. Bayesian inference and optimal design for the sparse linear model[J]. Journal of Machine Learning Research, 2008, 9: 759-813.
[18] BECK A, TEBOULLE M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J]. SIAM Journal on Imaging Sciences, 2009, 2(1): 183-202.
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