Bayesian Cram\'{e}r-Rao Bound for Noisy Non-Blind and Blind Compressed Sensing

摘要

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