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


This letter presents a maximum likelihood (ML)-primarily based algorithm for 2-dimensional (a pair of-D) direction-of-arrival (DOA) estimation based on the same rectangular array (URA). The new algorithm iteratively estimates the parameters in an exceedingly rough to fine manner, intervened with filtering processes to separate the signals into appropriate teams. To facilitate implementations of the ML estimation, the concept of Pincus and a Monte Carlo methodology called importance sampling (IS) are used to see the worldwide optimum ML answer. As such, the parameters will be exactly estimated with only moderate complexity. Moreover, the estimated parameters are automatically paired along while not extra computations. Simulation results show that the new algorithm outperforms the main state-of-the-art works and will achieve the Cramer-Rao lower certain (CRLB) even in low signal-to-noise ratio (SNR) eventualities.

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