Sparse signal recovery via multipath matching pursuit

In this paper, we propose a sparse recovery algorithm, termed multiple path matching pursuit (MMP), that improves the recovery performance of sparse signals. By investigating the multiple paths and then choosing the most promising path in the final moment, the MMP algorithm improves the chance of finding the true support and therefore enhances the recovery performance. From the restricted isometry property (RIP) analysis, we show that the MMP algorithm can perfectly reconstruct any K-sparse (K >1) signals, √provided that the sensing matrix satisfies RIP with δK+L < √ L/√ K +3√ L. We demonstrate by empirical simulations that the MMP algorithm is very competitive in both noisy and noiseless scenarios.