  We present a gridless sparse iterative covariance-based estimation method
based on alternating projections for direction-of-arrival (DOA) estimation. The
gridless DOA estimation is formulated in the reconstruction of
Toeplitz-structured low rank matrix, and is solved efficiently with alternating
projections. The method improves resolution by achieving sparsity, deals with
single-snapshot data and coherent arrivals, and, with co-prime arrays,
estimates more DOAs than the number of sensors. We evaluate the proposed method
using simulation results focusing on co-prime arrays.
