Robust Adaptive Beamforming using Bayesian Beamformer

초록

An adaptive beamforming system consists of an antenna array, combined with signal processing in both space and time. The most widely used adaptive filter is the least mean square (LMS) algorithm. Unfortunately, LMS and other gradient-based adaptive algorithms degrade badly when the filter is subject go input signals that are corrupted by impulsive noise. An adaptive beamformer that is robust to uncertainty in source direction-of ?arrival (DOA) is derived using a Bayesian approach. The DOA is assumed to be a discrete random variable with a known a priori probability density function (pdf) that reflects the level of uncertainty in the source DOA. The resulting beamformer is a weighted sum ofminimum variance distortionless response (MVDR) beamformers pointed at a set of candidate DOA?s, where the relative contribution of each MVDR beamformer is determined from the a posteriori pdf of the DOA conditioned on previously observed data. The Bayesian beamfomer is then a linear combination of adaptive MVDR beamformers weighted by the a posteriori pdf. This paper introduced that the robust adaptive beamforming by a Bayesian beamformer and adaptive beamforming by LMS algorithm.