Robust Adaptive Beamforming for Steering Vector Uncertainties Based on Equivalent Doas Method

The adaptive beamformers often suffer severe performance degradation when there exist uncertainties in the steering vector of interest. In this paper, we develop a new approach to robust adaptive beamforming in the presence of an unknown signal steering vector. Based on the observed data, we try to estimate an equivalent directionof-arrival (DOA) for each sensor, in which all factors causing the steering vector uncertainties are ascribed to the DOA uncertainty only. The equivalent DOA of each sensor can be estimated one by one with the assumption that the elements of the steering vector are uncorrelated with each other. Using a Bayesian approach, the equivalent DOA estimator of each sensor is a weighted sum of a set of candidate DOA’s, which are combined according to the value of the a posteriori probability for each pointing direction. In this way, the signal steering vector and the diagonal loading sample matrix inversion (DL-SMI) version adaptive beamformer can be obtained. Numerical simulations illustrate the robustness of the proposed beamforming algorithm.