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Published Online: 17 July 2019
Accepted: June 2019

Deep-learning source localization using multi-frequency magnitude-only data

The Journal of the Acoustical Society of America 146, 211 (2019); https://doi.org/10.1121/1.5116016
Haiqiang Niu1,a), Zaixiao Gong1, Emma Ozanich2, Peter Gerstoft2, Haibin Wang1, and Zhenglin Li1
more...View Affiliations
Abstract
A deep learning approach based on big data is proposed to locate broadband acoustic sources using a single hydrophone in ocean waveguides with uncertain bottom parameters. Several 50-layer residual neural networks, trained on a huge number of sound field replicas generated by an acoustic propagation model, are used to handle the bottom uncertainty in source localization. A two-step training strategy is presented to improve the training of the deep models. First, the range is discretized in a coarse (5 km) grid. Subsequently, the source range within the selected interval and source depth are discretized on a finer (0.1 km and 2 m) grid. The deep learning methods were demonstrated for simulated magnitude-only multi-frequency data in uncertain environments. Experimental data from the China Yellow Sea also validated the approach.

ACKNOWLEDGMENTS

This research was supported by the National Natural Science Foundation of China (Grant Nos. 11434012 and 11874061) and the Youth Innovation Promotion Association, Chinese Academy of Sciences.

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