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A Deep Neural Network Based Method of Source Localization in a Shallow Water Environment

This paper applies deep neural network (DNN) to source localization in a shallow water environment because of its powerful modeling capability and the little dependence on the prior knowledge of environmental parameters. The classical two-stage scheme is adopted, in which feature extraction and DNN analysis are independent steps. It firstly extracts the input feature from the observed signal received by underwater hydrophones. The eigenvectors associated with the modal signal space are decomposed from the covariance matrices of the data field at different frequencies, which are used as the input feature of DNN. The time delay neural network (TDNN) is exploited to model the long term feature representation and construct the regression model. The output is the source range-depth estimate. Several experiments using simulation and experimental data are conducted to evaluate the performance of the proposed method. The results demonstrate the effectiveness and potential of DNN for source localization. Particularly, experiments show that simulation data can be merged to train a general model for experimental data when lacking of sufficient training data in real-world environment.