By Topic

Adaptive compressive sampling using partially observable markov decision processes

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Ramin Zahedi ; Colorado State University, ECE Department, 1373 Campus Delivery, Fort Collins, 80523-1373, USA ; Lucas W. Krakow ; Edwin K. P. Chong ; Ali Pezeshki

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.

Published in:

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Date of Conference:

25-30 March 2012