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

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)

DOI: 10.12000/JR15056

钟金荣文贡坚,

(国防科技大学自动目标识别重点实验室长沙 410073)

计量
- 文章访问数: 3574
- HTML全文浏览量: 728
- PDF下载量: 1154
- 被引次数: 0
出版历程
- 收稿日期: 2015-05-11
- 修回日期: 2016-02-01
- 网络出版日期: 2016-02-28

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

(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)

摘要

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。
- 雷达信号处理 /
- 压缩感知雷达 /
- 块结构 /
- 压缩测量 /
- 稀疏重构
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.
- Radar signal processing /
- Compressive Sensing (CS) radar /
- Block structure /
- Compressed measurement /
- Sparse reconstruction

HTML全文

参考文献(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.