Deep Neural Network for Source Localization Using Underwater Horizontal Circular Array

This paper applies deep neural network (DNN) to source localization in a shallow water environment using underwater horizontal circular array. The proposed method can discriminate source locations in a three-dimension space. The proposed method adopts a two-stage scheme, incorporating feature extraction and DNN analysis. In feature extraction step, the eigenvectors corresponding to the modal signal space, which are shown to be able to represent the propagating modes of the sound source, are extracted as the input feature of DNN. The eigenvectors are obtained by applying eigenvalue decomposition (EVD) of the covariance matrix of the received multi-channel signal. In DNN analysis step, time delay neural network (TDNN) is used to construct the mapping relationship between the eigenvectors and the source locations, because it is capable of making use of sequential information of the source signal. The output of the network is the source location estimates. Several experiments are conducted to demonstrate the effectiveness of the proposed method.