Applications of optimal time-domain beamforming

Abstract

Applications to ocean acoustic data from a towed array and to speech processing are presented for an improved optimal time-domain beamformer, which involves optimizing over all possible source bearings and time series for multiple sources using simulated annealing. The convergence of the parameter search is accelerated by accepting time series perturbations only when the energy decreases. A comparison with the conventional delay-and-sum beamformer illustrates that the optimal beamformer handles larger receiver spacing and larger source-to-receiver ratio. Periodic ambiguities are essentially eliminated by using irregular receiver spacing and the improved search algorithm. Weak sources are handled with fractional beamforming. Noise cancellation is possible if the parameters of the noise are included in the search space. Two-dimensional localization is performed for nearby sources.