Wideband Direction-of-Arrival Estimation Based on Deep Learning

Abstract

The performance of traditional high-resolution direction-of-arrival (DOA) estimation methods is sensitive to the inaccurate knowledge on prior information, including the position of array elements, array gain and phase, and the mutual coupling between the array elements. Learning-based methods are data-driven and are expected to perform better than their model-based counterparts, since they are insensitive to the array imperfections. This paper presents a learning-based method for DOA estimation of multiple wideband far-field sources. The processing procedure mainly includes two steps. First, a beamspace preprocessing structure which has the property of frequency invariant is applied to the array outputs to perform focusing over a wide bandwidth. In the second step, a hierarchical deep neural network is employed to achieve classification. Different from neural networks which are trained through a huge data set containing different angle combinations, our deep neural network can achieve DOA estimation of multiple sources with a small data set, since the classifiers can be trained in different small subregions. Simulation results demonstrate that the proposed method performs well both in generalization and imperfections adaptation.

Keywords

direction-of-arrival (DOA) estimation / deep-neural network (DNN) / wideband / multiple sources / array imperfection