Sparse signal recovery using orthogonal matching pursuit (OMP)

Compressive sensing is an emergent field of signal processing which states that a small number of non-adaptive linear projections on a compressible signal contain enough information to reconstruct and process it. This paper presents the results of evaluating 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 subsequent analysis of their suitability for specific applications.