ADAPTIVE COMPRESSIVE SAMPLING USING PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES

被引：0

作者：

Zahedi, Ramin ^{[1
]}

Krakow, Lucas W. ^{[1
]}

Chong, Edwin K. P. ^{[1
]}

Pezeshki, Ali ^{[1
]}

机构：

[1] Colorado State Univ, ECE Dept, Ft Collins, CO 80523 USA

来源：

2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2012年

关键词：

Compressive sensing; POMDP; rollout; Q-value approximation; adaptive sensing;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

We present an approach to adaptive measurement selection in compressive sensing for estimating sparse signals. Given a fixed number of measurements, we consider the sequential selection of the rows of a compressive measurement matrix to maximize the mutual information between the measurements and the sparse signal's support. We formulate this problem as a partially observable Markov decision process (POMDP), which enables the application of principled reasoning for sequential measurement selection based on Bellman's optimality condition.

引用

页码：5269 / 5272

页数：4

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