Dynamic zero-point attracting projection for time-varying sparse signal recovery

Sparse signal recovery in the static case has been well studied under the framework of Compressive Sensing (CS), while in recent years more attention has also been paid to the dynamic case. In this paper, enlightened by the idea of modified-CS with partially known support, and based on a non-convex optimization approach, we propose the dynamic zero-point attracting projection (DZAP) algorithm to efficiently recover the slowly time-varying sparse signals.

Benefiting from the temporal correlation within signal structures, plus an effective prediction method of the future signal based on previous recoveries incorporated, DZAP achieves high-precision recovery with less measurements or larger sparsity level, which is demonstrated by simulations on both synthetic and real data, accompanied by the comparison with other state-of-the-art reference algorithms.