Fast Algorithm for Bayesian DOA Estimator Based on Metropolis-Hastings Sampling

Bayesian estimator is known to have the best performance in DOA estimation of narrowband sources. However, it is computationally intensive. In order to reduce its computational complexity, metropolis-hasting sampler, one of the most popular of Markov Monte Carlo methods, is employed to combine with it to propose a novel method called Bayesian DOA estimator based on metropolis-hasting sampling (MHB). In this method the power of the MHB spectrum function is viewed as the target distribution up to a constant proportionality, which is sampled by metropolis-hasting sampler. Simulations show that MHB not only keeps the excellent performance of Bayesian estimator but also reduces computational cost remarkably.