A Deep Network for Single-Snapshot Direction of Arrival Estimation

Abstract

This paper examines a deep feedforward network for beam-forming with the single-snapshot sample covariance matrix (SCM). The conventional beamforming formulation, typically quadratic in the complex weight space, is reformulated as real and linear in the weight covariance and SCM. The reformulated SCMs are used as input to a deep feed-forward neural network (FNN) for two source localization. Simulations demonstrate the effect of source incoherence and performance in a noisy tracking example. The FNN beamformer is experimentally tested on the Swellex96 experiment S95 source tow with a loud interferer.