A new backtracking-based sparsity adaptive algorithm for distributed compressed sensing

Abstract

A new iterative greedy algorithm based on the backtracking technique was proposed for distributed compressed sensing (DCS) problem. The algorithm applies two mechanisms for precise recovery soft thresholding and cutting. It can reconstruct several compressed signals simultaneously even without any prior information of the sparsity, which makes it a potential candidate for many practical applications, but the numbers of non-zero (significant) coefficients of signals are not available. Numerical experiments are conducted to demonstrate the validity and high performance of the proposed algorithm, as compared to other existing strong DCS algorithms.