Joint Estimation of Mutual Coupling and Direction of Arrival for 2D Sparse Arrays using CMA-ES

2D sparse arrays have better performance than uniformly-spaced arrays with the same number of elements when applied in direction of arrival (DOA) estimation problems. In practice, when these elements are closely spaced, mutual coupling effects distort the commonly assumed spatial model and compromise the standard DOA techniques. If the exact mutual coupling matrix (MCM) is not known a priori, the MCM and DOAs must be jointly estimated. This results in a difficult, nonlinear optimization problem. Nature inspired algorithms have been shown to be effective in complex electromagnetic optimization problems including 1D sparse array problems for DOA estimation. In this paper, covariance matrix adaptation evolution strategy (CMA-ES) is used to jointly compensate for mutual coupling and estimate direction of arrival for 2D sparse arrays. Results for a nested sparse array configuration are presented.