A Low Complexity Signal Recovery Algorithm Based on Compressed Sensing

Compressed sensing (CS) can give a sparse representation of compressible signals. We consider the problem of blind signal recovery based on CS and propose a novel algorithm for sparse signal reconstruction with low complexity. From the CS sampled measurements, we first get the signal parameters' estimation, such as the carrier frequency, and reconstruct the narrow band signal using the estimated result. In particular, we focus on its noise performance and get approximate analytical expressions of the output signal-to-noise ratio. Simulation results show that our proposed algorithm has good noise performance, as well as low computation complexity.