On Selection of Search Space Dimension in Compressive Sampling Matching Pursuit

被引：0
|
作者：
Ambat, Sooraj K. [1 ]
Chatterjee, Saikat [2 ]
Hari, K. V. S. [1 ]
机构：
[1] Indian Inst Sci, Dept Elect Commun Engg, Stat Signal Proc Lab, Bangalore 560012, Karnataka, India
[2] KTH Royal Inst Technol, Sch Elect Engn, Commun Theory Lab, S-10044 Stockholm, Sweden
来源：
TENCON 2012 - 2012 IEEE REGION 10 CONFERENCE: SUSTAINABLE DEVELOPMENT THROUGH HUMANITARIAN TECHNOLOGY | 2012年
关键词：
Compressed sensing; Sparse Recovery; Greedy Pursuit Algorithms; SIGNAL RECOVERY; RECONSTRUCTION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Compressive Sampling Matching Pursuit (CoSaMP) is one of the popular greedy methods in the emerging field of Compressed Sensing (CS). In addition to the appealing empirical performance, CoSaMP has also splendid theoretical guarantees for convergence. In this paper, we propose a modification in CoSaMP to adaptively choose the dimension of search space in each iteration, using a threshold based approach. Using Monte Carlo simulations, we show that this modification improves the reconstruction capability of the CoSaMP algorithm in clean as well as noisy measurement cases. From empirical observations, we also propose an optimum value for the threshold to use in applications.
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页数：5
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