Volume 5 Issue 1
Feb.  2016
Turn off MathJax
Article Contents
Abstract
References
Article Navigation >  Journal of Radars  > 2016  >  5(1): 99-108
Zhong Jinrong, Wen Gongjian. Compressive Sensing for Radar Target Signal Recovery Based on Block Sparse Bayesian Learning(in English)[J]. Journal of Radars, 2016, 5(1): 99-108. doi: 10.12000/JR15056
Citation: Zhong Jinrong, Wen Gongjian. Compressive Sensing for Radar Target Signal Recovery Based on Block Sparse Bayesian Learning(in English)[J]. Journal of Radars, 2016, 5(1): 99-108. doi: 10.12000/JR15056
shu

Compressive Sensing for Radar Target Signal Recovery Based on Block Sparse Bayesian Learning(in English)

DOI: 10.12000/JR15056

  • (Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China)

Funds:

The New Century Excellent Talents Supporting Plan of Ministry Education (No.NCET-11-0866)

  • Received Date: 2015-05-11
  • Rev Recd Date: 2016-02-01
  • Publish Date: 2016-02-28
    Abstract

  • Nowadays, high-speed sampling and transmission is a foremost challenge of radar system. In order to solve this problem, a compressive sensing approach is proposed for radar target signals in this study. Considering the block sparse structure of signals, the proposed method uses a simple measurement matrix to sample the signals and employ a Block Sparse Bayesian Learning (BSBL) algorithm to recover the signals. The classical BSBL algorithm is applicable to real signal, while radar signals are complex. Therefore, a Complex Block Sparse Bayesian Learning (CBSBL) is extended for the radar target signal reconstruction. Since the existed radar signal compressive sensing models do not take block structures in consideration, the signal reconstruction of proposed approach is more accurate and robust, and the simple measurement matrix leads to an easy implementation of hardware. The effectiveness of the proposed approach is demonstrated by numerical simulations.

     

    • FullText(HTML)
    • References(32)
  • [1]
    Du L, Wang P, Liu H, et al.. Bayesian spatiotemporal multitask learning for radar HRRP target recognition[J]. IEEE Transactions on Signal Processing, 2011, 59(7): 3182-3196.
    [2]
    Shi L, Wang P, Liu H, et al.. Radar HRRP statistical recognition with local factor analysis by automatic Bayesian Ying Yang harmony learning[C]. IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), Dallas Texas USA, 2010: 1878-1881.
    [3]
    Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
    [4]
    Baraniuk R and Steeghs P. Compressive radar imaging[C]. IEEE Radar Conference, Boston, Apr. 2007: 128-133.
    [5]
    Ender J H G. On compressive sensing applied to radar[J]. Signal Processing, 2010, 90(5): 1402-1414.
    [6]
    Xie X C and Zhang Y H. High-resolution imaging of moving train by ground-based radar with compressive sensing[J]. Electronics Letters, 2010, 46(7): 529-531.
    [7]
    Hereman M and Strohmer T. High-resolution radar via compressed sensing[J]. IEEE Transactions on Signal Processing, 2009, 57(6): 2275-2284.
    [8]
    Patel V M, Easley G R, Healy D M, et al.. Compressed synthetic aperture radar[J]. IEEE Journal of Selected Topics on Signal Processing, 2010, 4(2): 244-254.
    [9]
    Devore R A. Deterministic construction of compressed sensing matrices[J]. Journal of Complexity, 2013, 23(4): 918-925.
    [10]
    Ni K and Datta S. Efficient deterministic compressed sensing for images with chirps and reed-muller codes[J]. SIAM Journal on Imaging Sciences, 2011, 4(3): 931-953.
    [11]
    Li S X, Gao F, Ge G N, et al.. Deterministic construction of compressed sensing matrices via algebraic curves[J]. IEEE Transactions on Information Theory, 2012, 58(8): 5035-5041.
    [12]
    Abolghasemi V, Ferdowsi S, and Sanei S. A gradient-based alternating minimization approach for optimization of the measurement matrix in compressive sensing[J]. Signal Processing, 2012, 92(3): 999-1009.
    [13]
    Donoho D L and Tsaig Y. Sparse solution of underdetermined systems of linear equations by stage wise orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2012, 58(2): 1094-1121.
    [14]
    Baraniukr, Cevher V, Duarte M, et al.. Model-based compressive sensing[J]. IEEE Transactions on Information Theory, 2010, 56(4): 1982-2001.
    [15]
    Asaeia, Golbabaee M, Bourlard H, et al.. Structured sparsity models for reverberant speech separation[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2014, 22(3): 620-633.
    [16]
    Yuan M and Liu Y. Model selection and estimation in regression with grouped variables[J]. Journal of the Royal Statistical Society Series B, 2006, 68(1): 49-67.
    [17]
    Sun H, Zhang Z L, and Yu L. From sparseity to structured sparsity: Bayesian perspective[J]. Signal Processing, 2012, 28(6): 760-773 (in Chinese).
    [18]
    孙洪, 张智林, 余磊. 从稀疏到结构化稀疏: 贝叶斯方法[J]. 信号处理, 2012, 28(6): 760-773. Zhang Z L and Rao B D. Recovery of block sparse signals using the framework of block sparse Bayesian learning[C]. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, Mar. 2012: 3345-3348.
    [19]
    Zhang Z L and Rao B D. Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation[J]. IEEE Transactions on Signal Processing, 2012, 61(8): 2009-2015.
    [20]
    Babacan S D, Nakajima S, and Do M N. Bayesian group-sparse modeling and variational inference[J]. IEEE Transactions on Signal Processing, 2014, 62(11): 2906-2921.
    [21]
    Liu B Y, Zhang Z L, Xu G, et al.. Energy efficient telemonitoring of physiological signals via compressed sensing: a fast algorithm and power consumption evaluation[J]. Biomedical Signal Processing and Control, 2014, 11(1): 80-88.
    [22]
    Shen Y, Duan H, Fang J, et al.. Pattern-coupled sparse bayesian learning for recovery of block-sparse signals[J]. IEEE Transactions on Signal Processing, 2013, 63(2): 1896-1900.
    [23]
    Zhong J R, Wen G J, and Ma C H. Radar signal reconstruction algorithm based on complex block sparse Bayesian learning[C]. 12th International Conference on Signal Processing, Hangzhou, China, Oct. 2014: 1930-1933.
    [24]
    Davies M E and Gribonval R. Restricted isometry constants where lp sparse recovery can fail for 0[25] Wipf D P and Rao B D. Sparse Bayesian learning for basis selection[J]. IEEE Transactions on Signal Processing, 2004, 52(8): 2153-2164.
    [25]
    Potter L C and Moses R L. Attributed scattering centers for SAR ATR[J]. IEEE Transactions on Image Processing, 1997, 6(1): 79-91.
    [26]
    Potter L C, Chiang D M, Carriere R, el al.. A GTD-based parametric model for radar scattering[J]. IEEE Transactions on Antennas and Propagation, 1995, 43(10): 1058-1066.
    [27]
    Eldar Y C, Kuppinger P, and Bolcskei H. Block-sparse signals: uncertainty relations and efficient recovery[J]. IEEE Transactions on Signal Processing, 2010, 58(6): 3042-3054.
    [28]
    Eldar Y C and Mishali M. Robust recovery of signals from a structured union of subspaces[J]. IEEE Transactions on Information Theory, 2009, 55(11): 5302-5316.
    [29]
    Huang J Z and Zhang T. METAXAS D. Learning with dynamic structured sparsity[J]. Journal of Machine Learning Research, 2012, 12(7): 3371-3412.
    [30]
    Yu L, Sun H, Barbot J P, et al.. Bayesian compressive sensing for cluster structured sparse signals[J]. Signal Processing, 2012, 92(1): 259-269.
    [31]
    Peleg T, Eldar Y and Elad M. Exploiting statistical dependencies in sparse representations for signalrecovery[J]. IEEE Transactions on Signal Processing, 2012, 60(5): 2286-2303.
    [32]
    Steven M. K. Fundamentals of Statistical Signal Processing Volume I: Estimation Theory[M]. Englewood Cliffs, NJ, USA, Prentice Hall, IInc., 1993: 493-567. 罗鹏飞, 张文明等译. 统计信号处理基础估计与检测理论[M].北京: 电子工业出版社, 2006: 397-440.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(3699) PDF downloads(1164) Cited by()
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return