Bayesian Cramér-Rao Bound for Noisy Non-Blind and Blind Compressed Sensing

Details Bayesian Cramér-Rao Bound for Noisy Non-Blind and Blind Compressed Sensing Bayesian Cramér-Rao Bound for Noisy Non-Blind and Blind Compressed Sensing

2010-05-24

arxiv.org/abs/1005.4316v1

Computer Science

Information Theory

This paper was submitted at 2 June 2009 to IEEE Signal Processing Letters and was rejected at 21 August 2009

Scientific paper

In this paper, we address the theoretical limitations in reconstructing sparse signals (in a known complete basis) using compressed sensing framework. We also divide the CS to non-blind and blind cases. Then, we compute the Bayesian Cramer-Rao bound for estimating the sparse coefficients while the measurement matrix elements are independent zero mean random variables. Simulation results show a large gap between the lower bound and the performance of the practical algorithms when the number of measurements are low.

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