基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)

钟金荣文贡坚

钟金荣文贡坚, . 基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)[J]. 雷达学报, 2016, 5(1): 99-108. doi: 10.12000/JR15056
引用本文: 钟金荣文贡坚, . 基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)[J]. 雷达学报, 2016, 5(1): 99-108. doi: 10.12000/JR15056
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

基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)

(English)

doi: 10.12000/JR15056

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

(English)

Funds: 

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

  • 摘要: 高速采样和传输是目前雷达系统面临的一个重要挑战。针对这一问题,该文提出一种利用信号块结构特性的雷达目标压缩感知方法。该方法采用一个简单的测量矩阵对信号进行采样,然后运用块稀疏贝叶斯学习算法恢复信号。经典的块稀疏贝叶斯学习算法适用于实信号,该文将其扩为可直接处理雷达信号的复数域稀疏贝叶斯算法。相对于现有压缩感知方法,该方法不仅具有更好的信号重构精度和鲁棒性,更重要的是其压缩测量矩阵形式简单、易于硬件实现。数值仿真实验结果验证了该方法的有效性。
  • [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.
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  • 收稿日期:  2015-05-11
  • 修回日期:  2016-02-01
  • 刊出日期:  2016-02-28

基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)

(English)

doi: 10.12000/JR15056

摘要: 高速采样和传输是目前雷达系统面临的一个重要挑战。针对这一问题,该文提出一种利用信号块结构特性的雷达目标压缩感知方法。该方法采用一个简单的测量矩阵对信号进行采样,然后运用块稀疏贝叶斯学习算法恢复信号。经典的块稀疏贝叶斯学习算法适用于实信号,该文将其扩为可直接处理雷达信号的复数域稀疏贝叶斯算法。相对于现有压缩感知方法,该方法不仅具有更好的信号重构精度和鲁棒性,更重要的是其压缩测量矩阵形式简单、易于硬件实现。数值仿真实验结果验证了该方法的有效性。

English Abstract

钟金荣文贡坚, . 基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)[J]. 雷达学报, 2016, 5(1): 99-108. doi: 10.12000/JR15056
引用本文: 钟金荣文贡坚, . 基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)[J]. 雷达学报, 2016, 5(1): 99-108. doi: 10.12000/JR15056
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
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