Source localization using back propagation neural network and a single vector receiver

Abstract

Matched field processing (MFP) has been widely used in source localization in shallow waters, whose performance is strongly correlated with the knowledge of environmental properties and proper selection of sound propagation model. This paper presents a neural network based approach for source ranging of moving target, which does not need heavy sound field calculation and no requirement of environmental information as a known prior. This neural network is designed to determine source range by observing multiple-frequency sound fields as excited by the source and recorded on a single vector receiver. In synthetic tests, this neural network is first trained on training data set as to adaptively select the optimal features through error back propagation, and then its localization performance is validated on testing data set. This approach is then tested on ship noise data as collected by a single vector sensor, and the relative error is smaller than 0.1 through comparing to GPS calculations. The promising results suggest the proposed approach can be further developed for source ranging applications with light system.