Fast Algorithm for Maximum Likelihood DOA Estimation in MIMO Array

Maximum Likelihood estimator (ML) has shown excellent performance of Direction Of Arrival (DOA) estimation in Multiple Input Multiple Output (MIMO) array. However, the computation burden of MIMO ML is very large. In order to resolve this problem, a novel MIMO Maximum Likelihood DOA Estimation based on Metropolis-Hasting Sampling (MIMO MHML) is proposed, which combines Markov Monte Carlo method with MIMO Maximum Likelihood DOA estimator. MIMO MHML regards the power of the MIMO ML spectrum function as a target distribution up to a constant scalar, and uses Metropolis-Hasting sampler to sample from it. Simulation results show that MIMO MHML provides similar performance to that achieved by the MIMO ML method, but its computational cost is reduced greatly.