A Performance-Weighted Blended Dominant Mode Rejection Beamformer
John R. Buck, Andrew C. Singer · 2018
Adaptive beamformers operating in snapshot deficient situations often estimate regularization parameters such as the diagonal loading level or the signal subspace dimension. We propose a new beamformer that avoids this problem by computing its array weights as a mixture of the array weights for a set of beamformers. The new beamformer's average output power asymptotically approaches the best performance of any of the beamformers in the set. Applying this technique to the Dominant Mode Rejection (DMR) beamformer obviates the need to estimate the dominant signal subspace dimension. An example simulation of a complicated passive sonar scenario illustrates that the blended beamformer's performance rivals the performance of the best fixed subspace DMR beamformer, and may outperform all of them in nonstationary environments.