2-D Unconditional Maximum Likelihood DOA Estimation Based on Majorization-Minimization

In this paper, we propose a majorization-minimization (MM) based refinement strategy tailored for two-dimensional (2-D) unconditional maximum likelihood (UML) direction-of-arrival (DOA) estimation of a single source. We introduce two surrogate functions (linear and quadratic) for 2-D UML DOA estimation with the aim of successively reducing the objective function's value. The proposed MM method guarantees convergence to the objective function's stationary point. Furthermore, we employ the backtracking squared iterative method (SQUAREM) to accelerate the convergence speed of the proposed MM method. Numerical experiments further validate the efficiency of our proposed MM method.