Sparse signal recovery using orthogonal matching pursuit (OMP)
Adriana Patricia Lobato Polo, Rafael Humberto Ruiz Coral, Julián Armando Quiróga Sepúlveda, Adolfo León Recio Vélez
Abstract
Compressive sensing is an emergent field of signal processing which states that a small number of non-adaptive linear project- tions on a compressible signal contain enough information to reconstruct and process it. This paper presents the results of e- valuating five measurement matrices for applying them to compressive sensing in a system using orthogonal matching pursuit (OMP) to reconstruct the original signal. The measurement matrices were those implicated in compressive sensing as well as in reconstructing the signal. The Hadamard-random matrix stood out within this group of matrices because the lowest percentage of error in signal recovery was obtained with it. This paper also presents a methodology for evaluating these matrices, allowing sub- sequent analysis of their suitability for specific applications.
Keywords
compressed sensing; orthogonal matching pursuit; measurement matrix
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