Extensions to Orthogonal Matching Pursuit for Compressed Sensing
Compressed Sensing (CS) provides a set of mathematical results showing that sparse signals can be exactly reconstructed from a relatively small number of random linear measurements. A particularly appealing greedy-approach to signal reconstruction from CS measurements is the so called Orthogonal Matching Pursuit (OMP). The authors propose two modifications to the basic OMP algorithm, which can be handy in different situations. The Shannon-Nyquist sampling theorem states that the information content is preserved when the continuous-time analog signal is sampled at a rate that is at least twice the Fourier bandwidth of the signal.