Abstract
Universal adaptive beamformers (UABFs) combine classic adaptive beamformers with ensemble methods from machine learning to design new beamformers whose asymptotic performance rivals the performance of the best beamformer in a set. The UABF array weights blend a weighted average of the competing beamformer array weights based on past performance using the softmax function. This model averaging approach avoids a difficult model selection problem by hedging performance across a family of models rather than committing to a single model. This talk presents a UABF which blends covariance matrix tapers (CMT). The CMT technique creates broader notches in beampatterns for environments with moving interferers. Interferers moving with high bearing rates cross resolution cells faster and violate the stationarity solution of the minimum variance distortionless response (MVDR) beamformer. The performance of the CMT depends on the chosen notch width. The UABF adapts to find the blend of CMT notch widths that best balances the interferer output power with white noise gain. We present simulation results illustrating the benefits of the UABF approach for CMT in the presence of moving interferers. [Work supported by ONR 321US.]