【摘要】：Many problems arisen in research fields like systems biology and signal processing can be formulated as problems of block sparse signal recovery. Generally, it is known that to pursue the sparsest solution of an underdetermined system of linear equations is non-deterministic polynomial(NP)-hard. To solve block sparse recovery problems, the algorithm of block stagewise orthogonal matching pursuit(Block-StOMP) has been proposed to recover block sparse signals from compressed measurements, which is a greedy algorithm with satisfactory practical performance and some particularly interesting theoretical properties. In this paper, we propose an improved version of Block-StOMP, termed mBlock-StOMP. Specifically, mBlock-StOMP uses the estimated TDR(true discovery rate) to prune support sets of each stage in order to decrease FAR(false alarm rate) and pursue high recovery accuracy.Moreover, rigorous theoretical analysis for mBlock-StOMP is given in this paper. Compared with Block-StOMP, simulation results demonstrate that mBlock-StOMP outperforms Block-StOMP in terms of reconstruction accuracy without increasing computational burden significantly.
[Abstract]:Many problems arisen in research fields like systems biology and signal processing can be formulated as problems of block sparse signal recovery. Generally, it is known that to pursue the sparsest solution of an underdetermined system of linear equations is non-deterministic polynomial(NP)-hard. To solve block sparse recovery problems, the algorithm of block stagewise orthogonal matching pursuit(Block-StOMP) has been proposed to recover block sparse signals from compressed measurements, which is a greedy algorithm with satisfactory practical performance and some particularly interesting theoretical properties. In this paper, we propose an improved version of Block-StOMP, termed mBlock-StOMP. Specifically, mBlock-StOMP uses the estimated TDR(true discovery rate) to prune support sets of each stage in order to decrease FAR(false alarm rate) and pursue high recovery accuracy.Moreover, rigorous theoretical analysis for mBlock-StOMP is given in this paper. Compared with Block-StOMP, simulation results demonstrate that mBlock-StOMP outperforms Block-StOMP in terms of reconstruction accuracy without increasing computational burden significantly.
【作者單位】： Institute
【基金】：supported in part by the National Basic Research Program of China(973 Program)(2012CB316504,2009CB320602) the National Natural Science Foundation of China(61174122,61021063,60721003 and 60625305) the Specialized Research Fund for the Doctoral Program of Higher Education,China(20110002110045)
【分類號】：TN911.7

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 ZHAO Yao;FENG Jing;ZHANG BingChen;HONG Wen;WU YiRong;;Current progress in sparse signal processing applied to radar imaging[J];Science China(Technological Sciences);2013年12期

2 任建民;張義門;;A SPARSE MATRIX TECHNIQUE FOR SIMULATING SEMICONDUCTOR DEVICES AND ITS ALGORITHMS[J];Journal of Electronics(China);1990年01期

3 Yuli Fu;Jian Zou;Qiheng Zhang;Haifeng Li;;Recoverability analysis of block-sparse representation[J];Journal of Systems Engineering and Electronics;2014年03期

4 ZHANG WanHong;ZHOU Tong;HUANG BoXue;;Outlier deletion based improvement on the StOMP algorithm for sparse solution of large-scale underdetermined problems[J];Science China(Information Sciences);2014年09期

5 CEN YiGang;ZHAO RuiZhen;MIAO ZhenJiang;CEN LiHui;CUI LiHong;;A new approach of conditions on δ_(2s)(Φ) for s-sparse recovery[J];Science China(Information Sciences);2014年04期

6 ;Improved FOCUSS method for reconstruction of cluster structured sparse signals in radar imaging[J];Science China(Information Sciences);2012年08期

7 ;Performance improvement in multi-ship imaging for ScanSAR based on sparse representation[J];Science China(Information Sciences);2012年08期

8 ;Multi-polarimetric SAR image compression based on sparse representation[J];Science China(Information Sciences);2012年08期

9 Jia Li;Qiang Wang;Yi Shen;;Near optimal condition of OMP algorithm in recovering sparse signal from noisy measurement[J];Journal of Systems Engineering and Electronics;2014年04期

10 ;Ways to sparse representation:An overview[J];Science in China(Series F:Information Sciences);2009年04期

，

本文編號：2363113