SegBOMP: An efficient algorithm for block non-sparse signal recovery

Block sparse signal recovery methods have attracted great interests which take the block structure of the nonzero coefficients into account when clustering. Compared with traditional compressive sensing methods, it can obtain better recovery performance with fewer measurements by utilizing the block-sparsity explicitly. In this paper we propose a segmented-version of the block orthogonal matching pursuit algorithm in which it divides any vector into several sparse sub-vectors. By doing this, the original method can be significantly accelerated due to the dimension reduction of measurements for each segmented vector. Experimental results showed that with low complexity the proposed method yielded identical or even better reconstruction performance than the conventional methods which treated the signal in the standard block-sparsity fashion. Furthermore, in the specific case, where not all segments contain nonzero blocks, the performance improvement can be interpreted as a gain in “effective SNR” in noisy environment.