Side information based orthogonal matching pursuit in distributed compressed sensing

Compressed sensing (CS) theory shows that it is possible to reconstruct exactly a sparse signal from fewer linear measurements than that would be expected from traditional sampling theory. Orthogonal matching pursuit (OMP) is a kind of greedy pursuit algorithms that could implement CS signal recovery. However, in distributed compressed sensing (DCS) scenario, an emerging field based on the correlation among sources, original OMP has to be modified in order to satisfy the limited power restrictions. In this paper, by considering the reconstructed signal as side information (SI), we propose a new jointly decoding algorithm based on OMP and deduce the theoretical measurement rate of each signal. Compared with conventional decoding algorithms, a certain amount of saving in time and number of measurements is obtained and illustrated in the simulation results.