A FNN for source range and ocean environment classification using time-domain features

Abstract

Acoustic source ranging in an uncertain ocean environment is a complicated problem, though classification and regression-based machine learning algorithms show promise. A feedforward neural network (FNN) has been trained to do either classification or regression on both the source-receiver range and environment type using extracted time-domain features. Time waveforms are generated to simulate signals received at different ranges in three different environments with a sandy, muddy, or mixed sediment bottom. Four features are extracted from these waveforms: peak level, integrated level, signal length, and later decay time. These four features are used to train FNN for both classification and regression of range and environment type, and the results are compared to a network trained on the time waveforms. For small amounts of training data, the extracted features provide a higher accuracy than the full waveform. Thus, physics-based feature selection via preprocessing can lead to fairly accurate results when using a FNN with small datasets. These results lay a foundation for comparisons to the more computationally expensive convolutional neural networks. [Work supported by the Office of Naval Research.]