Source Ranging Using Ensemble Convolutional Networks in the Direct Zone of Deep Water

Yi-Ning Liu; Hai-Qiang Niu; Zheng-Lin Li

doi:10.1088/0256-307X/36/4/044302

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Source Ranging Using Ensemble Convolutional Networks in the Direct Zone of Deep Water

¹State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190
²University of Chinese Academy of Sciences, Beijing 100190

Funds: Supported by the National Natural Science Foundation of China under Grant Nos 11434012 and 11874061.

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Received Date: January 13, 2019

Published Date: March 31, 2019

Abstract

Abstract

Using deep convolutional neural networks as primary learners and a deep neural network as meta-learner, source ranging is solved as a regression problem with the ensemble learning method. Simulated acoustic data from the acoustic propagation model are used as the training data. Real data from an experiment in the South China Sea are used as the test data to demonstrate the performance. The results indicate that in the direct zone of deep water, signals received by a very deep receiver can be used to estimate the range of underwater sound source. Within 30 km, the mean absolute error of the range predictions is 1.0 km and the mean absolute percentage error is 7.9%.

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Source Ranging Using Ensemble Convolutional Networks in the Direct Zone of Deep Water

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