Abstract: Abstract In order to well ensure reconstruction of the original signal, the traditional Nyquist sampling theorem requires that the sampling rate must be twice as much the highest frequency of the original signal at least, which causes a tremendous amount of calculation and the waste of resources. But the compressive sensing theory describes that we can reconstruct the original signal from a small amount of random sampling as long as the signal is sparse or compressible.Based on the research and summarization of the traditional matching algorithm, this paper presented a new adaptive space orthogonal matching pursuit algorithm (ASO MP) for the reconstruction of the sparse signal. I}his algorithm introduces an regularized adaptive and spatial matching principle for the choice of matching atoms with reverse thinking,which accelerates the matching speed of the atom and improves the accuracy of the matching, ultimately leads to exact reconstruction of the original signal. Finally, we compared the ASOMP algorithm with the traditional MP and OMP algorithm under the software simulation. Experimental results show that the ASOMP reconstruction algorithm is superior to traditional MP and OMP algorithm on the reconstruction quality and the speed of the algorithm.

Key words: Compressive sensing, Sparse signal, Matching pursuit, Reconstruction algorithm