Adaptive compressive sampling using partially observable markov decision processes | IEEE Conference Publication | IEEE Xplore

Adaptive compressive sampling using partially observable markov decision processes


Abstract:

We present an approach to adaptive measurement selection in compressive sensing for estimating sparse signals. Given a fixed number of measurements, we consider the seque...Show More

Abstract:

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.
Date of Conference: 25-30 March 2012
Date Added to IEEE Xplore: 30 August 2012
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Conference Location: Kyoto, Japan

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