Adaptive compressive sampling using partially observable markov decision processes

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 th...

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Bibliographische Detailangaben

Hauptverfasser:	Zahedi, R., Krakow, L. W., Chong, E. K. P., Pezeshki, A.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	adaptive sensing Compressive sensing Libraries Linear programming Markov processes Mathematical model Mutual information POMDP Q-value approximation rollout Signal to noise ratio Vectors
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