Fast OMP Algorithm Based on Bayesian Test for Multiple Measurement Vectors Model

LI Shaodong; CHEN Wenfeng; YANG Jun; MA Xiaoyan

doi:10.11999/JEIT151131

Volume 38 Issue 7

Jul. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(7): 1731-1737

LI Shaodong, CHEN Wenfeng, YANG Jun, MA Xiaoyan. Fast OMP Algorithm Based on Bayesian Test for Multiple Measurement Vectors Model[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1731-1737. doi: 10.11999/JEIT151131

Citation:

LI Shaodong, CHEN Wenfeng, YANG Jun, MA Xiaoyan. Fast OMP Algorithm Based on Bayesian Test for Multiple Measurement Vectors Model[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1731-1737. doi: 10.11999/JEIT151131

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Fast OMP Algorithm Based on Bayesian Test for Multiple Measurement Vectors Model

doi: 10.11999/JEIT151131 cstr: 32379.14.JEIT151131

Received Date: 2015-10-10
Rev Recd Date: 2016-02-25
Publish Date: 2016-07-19

Abstract

Abstract

There are two issues in the Sparse Reconstruction (SR) algorithm of Multiple Measurement Vectors (MMV). One is the high computation complexity and the other is that redundant support set can not be effectively removed. In order to improve the efficiency and accuracy of SR algorithm simultaneously for MMV model, a Fast Orthogonal Matching Pursuit algorithm based on Bayesian Test (FOMP-BT) is presented in this paper. Firstly, the total number of iterations and the computation of each iteration are reduced through the new atomic group selection and warm start matrix inversion, thus the efficiency of the algorithm is improved. Secondly, using the idea of the Bayesian test to eliminate redundant support set, the accuracy of reconstruction is improved. Finally, the theoretical analysis of the algorithm is carried out from the aspects of parameter selection and computation complexity. The simulation results show that the proposed algorithm has the advantages of high accuracy, fast speed and good robustness to noise.
- Multiple measurement vectors model,
- Fast Orthogonal Matching Pursuit (FOMP) algorithm,
- Number of iterations,
- Bayesian test

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References(26)

References

DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.

ELDAR Y C. Sampling Theory Beyond Band Limited Systems[M]. Cambridge University Press, 2015: 1-8. doi: http: //dx.doi.org/10.1017/CBO9780511762321.

SHANE F C, BHASKAR D R, KJERSTI E, et al. Sparse solutions to linear inverse problems with multiple measurement vectors[J]. IEEE Transactions on Signal Processing, 2005, 53(7): 2477-2488.

CHEN Jie and HUO Xiaoming. Theoretical results on sparse representations of multiple-measurement vectors[J]. IEEE Transaction on Signal Processing, 2006, 54(12): 4634-4643.

陈一畅, 张群, 陈校平, 等. 多重测量矢量模型下的稀疏步进频率SAR成像算法[J]. 电子与信息学报, 2014, 36(12): 2987-2993. doi: 10.3724/SP.J.1146.2013.01831.

CHEN Yichang, ZHANG Qun, CHEN Xiaoping, et al. Imaging algorithm of sparse stepped fequency SAR based on multiple measurement vectors model[J]. Journal of Electuonics Information Technology, 2014, 36(12): 2987-2993. doi: 10.3724/SP.J.1146.2013.01831.

HEKHAR S, PATEL V M, NASRABADI N M , et al. Joint sparse representation for robust multimodal biometrics recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(1): 113-126.

李志林, 陈后金, 李居朋, 等. 一种有效的压缩感知图像重建算法[J]. 电子学报, 2011, 39(12): 2796-2800.

LI Zhilin, CHEN Houjin, LI Jupeng, et al. An eficient algorithm for compressed sensing image reconstruction[J]. Acta Electronica Sinica, 2011, 39(12): 2796-2800.

ZHAO Tan and NEHORAI A. Sparse direction of arrival estimation using co-prime arrays with off-grid targets[J]. IEEE Signal Processing Letters, 2014, 21(1): 26-29.

王泽涛, 段克清, 谢文冲, 等. 基于SA-MUSIC 理论的联合稀疏恢复STAP算法[J]. 电子学报, 2015, 43(5): 846-853.

WANG Zetao, DUAN Keqing, XIE Wenchong, et al. A joint sparse recovery STAP method based on SA-MUSIC[J]. Acta Electronica Sinica, 2015, 43(5): 846-853.

TAO Yu, ZHANG Gong, and ZHANG Jindong. Guaranteed stability of sparse recovery in distributed compressive sensing MIMO Radar[J]. International Journal of Antennas and Propagation, 2015, Article ID 421740: 1-10.

JIN Yuzhe and RAO B D. Support recovery of sparse signals in the presence of multiple measurement vectors[J]. IEEE Transaction on Information Theory, 2013, 59(5): 3139-3156.

王法松, 张林让, 周宇. 压缩感知的多重测量向量模型与算法分析[J]. 信号处理, 2012, 28(6): 785-792.

WANG Fasong, ZHANG Linrang, and ZHOU Yu. Multiple measurement vectors for compressed sensing: model and algorithms analysis[J]. Journal of Signal Processing, 2012, 28(6): 785-792.

GUAN Gui, WAN Qun, and ADACHI F. Direction of arrival estimation using modified orthogonal matching pursuit algorithm[J]. International Journal of the Physical Sciences, 2011, 6(22): 5230-5234.

BLANCHARD J D, CERMAK M, HANLE D, et al. Greedy algorithms for joint sparse recovery[J]. IEEE Transactions on Signal Processing, 2014, 62(7): 1694-1704.

ZHANG Y, YE Z F, XU X, et al. Off-grid DOA estimation using array covariance matrix and block-sparse Bayesian learning[J]. Signal Processing, 2014, 98: 97-201.

张贤达. 矩阵分析与应用(第2版)[M]. 北京: 清华大学出版社, 2013: 54-64.

ZHANG Xianda. Matrix Analysis and Applications (Second Edition)[M]. Beijing: Tsinghua University Press, 2013: 54-64.

ZHANG Jingxiong and YANG Ke. Informational analysis for compressive sampling in radar imaging[J]. Sensors 2015, 15(4): 7136-7155. doi: 10.3390/s150407136.

甘伟, 许录平, 苏哲, 等. 基于贝叶斯假设检验的压缩感知重构[J]. 电子与信息学报, 2011, 33(11): 2640-2646. doi: 10. 3727/SP.J.1146.2011.00151.

GAN Wei, XU Luping, SU Zhe , et al. Bayesian hypothesis testing based recovery for compressed sensing[J]. Journal of Electronics Information Technology, 2011,33(11): 2640-2646. doi: 10.3727/SP.J.1146.2011.00151.

杨成, 冯巍, 冯辉, 等. 一种压缩采样中的稀疏度自适应子空间追踪算法[J]. 电子学报, 2010, 38(8): 1914-1917.

YANG Cheng, FENG Wei, FENG Hui, et al. A sparsity adaptive subspace pursuit algorithm for compressive sampling[J]. Acta Electronica Sinica, 2010, 38(8): 1914-1917.

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