Bayesian high resolution DOA estimator based on importance sampling

Abstract

DOA estimation is an important research area in array signal processing. Bayesian maximum a posterior probability density DOA estimator (BM DOA estimator) has been shown to perform excellently. However, the BM estimator 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 BM DOA estimator. A novel Bayesian maximum a posterior DOA estimator based on importance sampling (ISBM DOA estimator) is proposed in this paper. ISBM DOA estimator not only keeps the good performance of the original BM DOA estimator, but also reduces the computation obviously because it needs not multidimensional search and reduces the computational complexity of the original method from O(L/sup K/) to O(K/spl times/H). Simulation results show that ISBM DOA estimator keeps the excellent performance of BM DOA estimator, but also reduces the computation evidently and performs better than MLE, MUSIC and MiniNorm, especially in low SNRs.