Maximum likelihood DOA estimator based on importance sampling

Maximum Likelihood Estimator (MLE) has been shown to be the best performance in parameter estimation. However, the computation burden of MLE is very large. In order to resolve the question of computation burden, Monte Carlo methods are combined with maximum likelihood DOA estimator. A novel Maximum Likelihood DOA Estimator based on Importance Sampling (ISMLE) is proposed. ISMLE not only keeps the excellent performance of the original MLE, but also reduces the computation greatly, from the computational complexity O(L^K) of original method to O(K×H).