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arXiv · 1005.4316

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

Hadi Zayyani·Massoud Babaie-Zadeh·Christian Jutten
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Abstract

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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Hadi Zayyani, Massoud Babaie-Zadeh, Christian Jutten. 2010-05-24. Bayesian Cramér-Rao Bound for Noisy Non-Blind and Blind Compressed Sensing. https://arxiv.org/abs/1005.4316

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