Signal reconstruction based on compressive sensing

Compressive sensing(CS)is a novel signal sampling theory under the condition that the signal is sparse or compressible.It has the ability of compressing a signal during the process of sampling.Using compressive sensing theory,one can reconstruct sparse or compressible signals accurately from a very limited number of measurements.This paper surveys the theoretical framework and the key technical problems of compressed sensing and introduces signal sparse representation,measurement matrix and reconstruction algorithms.In the end,realizes signal reconstruction and analyses the performances of Orthogonal Matching Pursuit(OMP)reconstruction algorithms.