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
收藏
页码：5269 / 5272
页数：4
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