  A new sparse signal recovery algorithm for multiple-measurement vectors (MMV)
problem is proposed in this paper. The sparse representation is iteratively
drawn based on the idea of zero-point attracting projection (ZAP). In each
iteration, the solution is first updated along the negative gradient direction
of an approximate $\ell_{2,0}$ norm to encourage sparsity, and then projected
to the solution space to satisfy the under-determined equation. A variable step
size scheme is adopted further to accelerate the convergence as well as to
improve the recovery accuracy. Numerical simulations demonstrate that the
performance of the proposed algorithm exceeds the references in various
aspects, as well as when applied to the Modulated Wideband Converter, where
recovering MMV problem is crucial to its performance.
