Robust steering vector mismatch technique for the multistage Wiener filter

This paper examines the impact of steering vector mismatch on the multistage Wiener filter (MWF). Since the NWF centrally features the steering vector in its formulation it is important to assess the impact of steering vector mismatch. Furthermore, since the MWF converges to the full rank MVDR solution, we would expect that steering vector mismatch would lead to signal cancellation (as with full rank MVDR) when the signal is present in the training data. We examine several standard techniques for increasing robustness and show how they apply to the MWE These include derivative constraints, quiescent pattern control (QPC) techniques, and covariance matrix tapers (CMT). We show that a combination of CMT and QPC, denoted CMTQ, is very effective at mitigating the impact of steering vector mismatch. Use of the CMTQ augmentation provides the steering vector mismatch robustness that we desire while retaining the reduced rank and reduced sample support characteristics of the MWE.