Backtracking based matching pursuit method for distributed compressed sensing
In this paper, we study a distributed compressed sensing (DCS) problem in which we need to recover a set of jointly sparse vectors from the measurements. A Backtracking based Adaptive Orthogonal Matching Pursuit (BAOMP) method to approximately sparse solutions for DCS is proposed. It is an iterative approach where each iteration consists of consecutive forward selection to adaptively choose several atoms and backward removal stages to detect the previous chosen atoms' reliability and then delete the unreliable atoms at each iteration. Also, unlike its several predecessors, the proposed method does not require the sparsity level to be known as a prior which makes it a potential candidate for many practical applications, when the sparsity of signals is not available. We demonstrate the reconstruction ability of the proposed algorithm on both synthetically generated data and image using Normal and Binary sparse signals, and the real life electrocardiography (ECG) data, where the proposed method yields less reconstruction error and higher exact recovery rate than other existing DCS algorithms.
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