Parameter estimation from compressed and sparse measurements
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Abstract
In this dissertation, the problem of parameter estimation from compressed and sparse noisy measurements is studied. First, fundamental estimation limits of the problem are analyzed. For that purpose, the effect of compressed sensing with random matrices on Fisher information, the Cramer-Rao Bound (CRB) and the Kullback-Leibler divergence are considered. The unknown parameters for the measurements are in the mean value function of a multivariate normal distribution. The class of random compression matrices considered in this work are those whose distribution is right-unitary invariant. The ...
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