Robust adaptive beamforming using a Bayesian steering vector error model

We propose a Bayesian approach to robust adaptive beamforming which entails considering the steering vector of interest as a random variable with some prior distribution. The latter can be tuned in a simple way to reflect how far is the actual steering vector from its presumed value. Two different priors are proposed, namely a Bingham prior distribution and a distribution that directly reveals and depends upon the angle between the true and presumed steering vector. Accordingly, a non-informative prior is assigned to the interference plus noise covariance matrix R, which can be viewed as a means to introduce diagonal loading in a Bayesian framework. The minimum mean square distance estimate of the steering vector as well as the minimum mean square error estimate of R are derived and implemented using a Gibbs sampling strategy. Numerical simulations show that the new beamformers possess a very good rate of convergence even in the presence of steering vector errors

Effective estimation of the desired-signal subspace and its application to robust adaptive beamforming

An effective method is proposed to estimate the desired-signal (S) subspace by the intersection between the signal-plus-interference (SI) subspace and a reference space covering the angular region where the desired signal is located. The estimated S subspace is robust to steering vector mismatch and overestimation of the SI subspace, capable of detecting the relative strength of the desired signal. And even the basis of the estimated S subspace can serve as an effective estimation of the steering vector of the desired signal. With these properties, the estimated S subspace can help to select a more accurate narrow area for searching for the steering vector of the desired signal in mismatch cases. The proposed method is applied for robust adaptive beamforming with an improved performance, as demonstrated by simulation results