A combination approach for compressed sensing signal reconstruction

Abstract

This paper presents a combination approach which fusing the estimates of forward backward pursuit (FBP) and backtracking-based adaptive orthogonal matching pursuit (BAOMP) to approximate sparse solutions for compressed sensing without the sparsity level as a prior. This algorithm referred to as combination approach for compressed sensing (CACS). It can improve the sparse signal recovery performance in a minimum number of measurements. Numerical experiments for both synthetic and real signals are conducted to demonstrate the validity and high performance of the proposed algorithm, as compared to the individual compressed sensing algorithms.