Maximum Likelihood 2-D DOA Estimation via Signal Separation and Importance Sampling

This letter presents a maximum likelihood (ML)-based algorithm for two-dimensional (2-D) direction-of-arrival (DOA) estimation based on a uniform rectangular array (URA). The new algorithm iteratively estimates the parameters in a rough to fine manner, intervened with filtering processes to separate the signals into appropriate groups. To facilitate implementations of the ML estimation, the theorem of Pincus and a Monte Carlo method known as importance sampling (IS) are employed to determine the global optimum ML solution. As such, the parameters can be precisely estimated with only moderate complexity. Moreover, the estimated parameters are automatically paired together without extra computations. Simulation results show that the new algorithm outperforms the main state-of-the-art works and can achieve the Cramer-Rao lower bound (CRLB) even in low signal-to-noise ratio (SNR) scenarios.