Abstract
It is extremely important to choose the optimal support set atoms in the reconstruction algorithm based on compressed sensing. The Look Ahead Orthogonal Matching Pursuit (LAOMP) is a commonly used reconstruction algorithm. However, in most cases, with the sparsity of signal unknown, we search every time for the fixed L iteration times not to guarantee that the selected atoms be optimal. To solve this problem, the Sparsity Adapitive Look Ahead Orthogonal Matching Pursuit (SALAOMP) is proposed. Simulation results show that its Average Support Cardinality Error (ASCE) performance and Exact Reconstruction Probability (ERP) performance are superior to the LAOMP algorithm.
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