Neural MCA for robust beamforming
Simone Fiori, Francesco Piazza · 2002
This paper aims at recalling recent results about neural Minor Component Analysis and to apply them to spatial adaptive array filtering (adaptive beamforming). The constrained beamformer power optimization principle is employed, which allows us to improve the performances of simpler beamforming algorithms by emphasizing white noise sensitivity control and prior knowledge about the disturbances.