Fusion of Matching Pursuits for Compressed Sensing Signal Reconstruction

Details Fusion of Matching Pursuits for Compressed Sensing Signal Reconstruction Fusion of Matching Pursuits for Compressed Sensing Signal Reconstruction

2012-04-20

arxiv.org/abs/1204.4656v1

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Matching Pursuits are very popular in Compressed Sensing for sparse signal recovery. Though many of the Matching Pursuits possess elegant theoretical guarantees for performance, it is well known that their performance depends on the statistical distribution of the non-zero elements in the sparse signal. In practice, the distribution of the sparse signal may not be known {\em a priori}. It is also observed that performance of Matching Pursuits degrades as the number of available measurements decreases from a threshold value which is method dependent. To improve the performance in these situations, we introduce a novel fusion framework for Matching Pursuits and also propose two algorithms for sparse recovery. Through Monte Carlo simulations we show that the proposed schemes improve sparse signal recovery in clean as well as noisy measurement cases.

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