Sparse Signal Recovery via Improved Sparse Adaptive Matching Pursuit Algorithm

Abstract

The accurate reconstruction of a signal within a reasonable period is the key process that enables the application of compressive sensing in large-scale image transmission. The sparsity adaptive matching pursuit (SAMP) algorithm does not need prior knowledge on signal sparsity and has high reconstruction accuracy but has low reconstruction efficiency. To overcome the low reconstruction efficiency, we propose the use of the fast segmentation sparsity adaptive matching pursuit (FSSAMP) algorithm, where the value of K estimated in each iteration increases in a nonlinear manner instead of undergoing linear growth. This form can reduce the number of iterations by accurate signal sparsity degree evaluation. In addition, we use signal segmentation strategies in the proposed algorithm to improve the algorithm accuracy. Experimental results demonstrated that the FSSAMP algorithm has more stable reconstruction performance and higher reconstruction accuracy than the SAMP algorithm.