Secondary sample based Bayesian beamforming

The adaptive beamformers often suffer severe performance degradation when there is a mismatch between the actual and presumed direction-of-arrival (DOA) of the desired signal. A Bayesian approach based on secondary sample to robust adaptive beamforming was proposed. Whether or not conducting the secondary sample is based on the distribution of the a posteriori probability of a set of candidate DOAs, which can be calculated from the received array signals. The resulting beamformer is a weighted sum of minimum variance distortionless response (MVDR) beamformers pointing at the latest set of DOAs, which are combined according to the value of the a posteriori probability for each pointing direction. Compared with the typical Bayesian approach to robust adaptive beamforming, the simulation on the case that the actual DOA is out of the region covered by the a priori DOA discrete set demonstrates that the proposed approach has obvious advantage in term of the array output signal-to-interference-plus-noise ratio (SINR).