Abstract
In this paper, we improve greedy algorithms to recover sparse signals with complex Gaussian distributed nonzero elements, when the probability of sparsity pattern is known a priori. By exploiting this prior probability, we derive a correction function that minimizes the probability of incorrect selection of a support index at each iteration of the orthogonal matching pursuit (OMP). In particular, we employ the order statistics of exponential distribution to create the correction function. Simulation results demonstrate that the correction function significantly improves the recovery performance of OMP and subspace pursuit (SP) for random Gaussian and Bernoulli measurement matrices.
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