Multiple Hypothesis Testing for Dynamic Support Recovery

被引：0

作者：

Qiao, Heng ^{[1
]}

Pal, Piya ^{[1
]}

机构：

[1] Univ Calif San Diego, Dept Elect & Comp Engn, San Diego, CA 92103 USA

来源：

2017 IEEE 18TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC) | 2017年

关键词：

Chernoff bound; support recovery; Markov chain; multiple hypothesis testing; multiple measurement vector; probability of error; SIMULTANEOUS SPARSE APPROXIMATION; SIGNAL RECONSTRUCTION; MEASUREMENT VECTORS; SENSOR ARRAYS; REPRESENTATIONS; LOCALIZATION; PERFORMANCE; ALGORITHMS; PURSUIT;

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：

This paper considers the problem of dynamic support recovery for jointly sparse signals from underdetermined measurements. Unlike Multiple Measurement Vector (MMV) models that assume a fixed support for all time instances, we allow the support to vary temporally following a finite state Markov chain. Instead of using l(1) minimization based algorithms, we cast the problem of dynamic support recovery as a multiple-hypothesis testing problem and analyze its performance. We derive an upper bound on the probability of error for the optimal decision rule which explicitly highlights the role of the time-varying priors associated with the Markov Chain. The results are valid for any transition probability matrix and choice of initial priors, and can also be used to study the MMV model as a special case.

引用

页数：5

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