Research Of Block Sparse Signals Reconstruct Algorithm Based On Compressive Sensing

In recent years, Compressed Sensing (CS) theory has attracted wide attention ofresearchers. It breaks through the limitations of traditional Shannon/Nyguist samplingtheorem in the field of signal processing, and greatly reduces the sampling requirements. Ithas been widely used in medical imaging, image processing, radar detection, patternrecognition, etc. Signal reconstruction is an important part of Compressed Sensing theory, butthe premise of signal reconstruction based on Compressed Sensing is that the signal is sparseor it can be converted to sparse signal. Sparse signal is a kind of signal that has only littlenonzero entries and the locations of the nonzero entries are random. Actually, most of thesparse signal has intrinsic structure. Recently, block sparse signal with nonzero entriesappeared in clusters has been a hot area of research.From the theory of compressed sensing, we have done intense research on block sparsesignal reconstruction algorithm. First we have introduced three widely studied block sparsesignal reconstruction algorithm which are the mixedl2/l1Norm Optimization Program(L-OPT), the block matching pursuit (BMP) and the block orthogonal matching pursuit(BOMP). The block orthogonal matching pursuit algorithm has been widely used for blocksparse signal in the reconstruction of compressed sensing. Then in this paper we haveproposed three new algorithms for improving the performance of the block orthogonalmatching pursuit algorithm. They are block orthogonal matching pursuit algorithm based onlook ahead strategy (LABOMP), block orthogonal matching pursuit algorithm based onorthogonal projection (PBOMP) and new algorithm combined them (PLABOMP). Theselection of atoms are vital for the BOMP algorithm, however the outcome of BOMPalgorithm maybe not optimum because it only chooses the local optimum atom in eachiteration. For LABOMP algorithm, the selection of atoms in the current iteration depend on itseffect on the future iterations, it provides a better performance. For the standard BOMP, theinner product is used to select a block of atom in each iteration which can’t get the mostcorrelated atoms. For PBOMP algorithm, a set of potential atoms are chosen and then a singleblock is finally selected based on orthogonal projection. PLABOMP is an algorithm combinesLABOMP and PBOMP algorithms such that a trade off between computational complexityand reconstruction performance can be established. Experiment has shown that the proposedthree new algorithms in this paper perform much better than BOMP algorithm for the signalto reconstruction noise ratio and time complexity.It has been observed that none of the block sparse signal reconstruction algorithmsoutperforms all others in the reconstruction performance and computational complexity. For this problem, we propose a fusion algorithm for block sparse signal reconstruction algorithm(BlockFA). It uses all viable algorithms and fuses their output sparse signal estimates todetermine a final signal estimate. The main advantage of BlockFA is that the participatingalgorithms need not require any modification and we can gain more reliable information thanthe information gained from each participate algorithm. Through experiment comparision, wefind that the proposed algorithm achieve a better reconstruction performance than anyparticipating algorithm.