Sparse signal recovery from compressed measurements using hybrid particle swarm optimization

The computationally intensive part of compressed sensing (CS) deals with the sparse signal reconstruction from lesser number of random projections. Finding sparse solution to such an underdetermined system is highly ill-conditioned and therefore requires additional regularization constraints. This research paper introduces a new approach for recovering a K-sparse signal from compressed samples using particle swarm optimization (PSO) along with separable surrogate functionals (SSF) algorithm. The suggested hybrid mechanism applied with appropriate regularization constraints speeds up the convergence of PSO. The estimated original sparse signal is also recovered with great precision. Simulation results show that the signal estimated with PSO-SSF combination outperforms the signal recovery through PSO, SSF and parallel coordinate descent (PCD) methods in terms of reconstruction accuracy. Finally, the efficiency of the proposed algorithm is validated experimentally by exactly recovering a one-dimensional K-sparse signal from only a few number of non-adaptive random measurements.