Gridless Sparse Direction Finding Method for Correlated Signals with Gain-Phase Errors

Abstract

In practical array systems, the gain-phase errors among antennas degrade the performance of direction finding significantly. In this paper, a novel sparse system model for direction of arrival (DOA) estimation in the scenario with gain-phase errors is proposed by exploiting the signal sparsity in the spatial domain. In contrast to the existing sparse-based methods using the grids to construct the dictionary matrix, a novel gridless method based on atomic norm and convex optimization is proposed, where the gain-phase errors are described by a diagonal matrix. With the Schur complement, a semidefinite programming is formulated from the optimization problem, and can be solved efficiently. With the gain-phase errors, the corresponding Cram’er-Rao lower bound (CRLB) of direction finding is derived as an estimation benchmark. Simulation results show that the proposed method performs better than the state-of-the-art methods in the scenario with correlated signals and gain-phase errors.