Maximum likelihood DOA estimator based on importance sampling

DOA estimation is an important research area in array signal processing. Maximum Likelihood Estimator (MLE) has been shown to perform best among all the methods. However, the MLE requires a multidimensional grid search and the computational burden increases exponentially with the dimension. So it is difficult to be used in realtime applications. In order to reduce the computation, Monte Carlo methods are combined with MLE. A novel Maximum Likelihood DOA Estimator based on Importance Sampling (ISMLE) is proposed. ISMLE not only reduces the computational complexity of the original MLE from O(LK) to O(KXH), but also keeps the perfect original performance of the MLE. Simulation results show that ISMLE keeps the excellent performance of MLE, and simultaneously it reduces the computation obviously. Also ISMLE performs better than MUSIC and MiniNorm, especially in low SNRs.