Deep-learning source localization using multi-frequency magnitude-only data
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.
Keywords
Cite
@article{arxiv.1903.12319,
title = {Deep-learning source localization using multi-frequency magnitude-only data},
author = {Haiqiang Niu and Zaixiao Gong and Emma Ozanich and Peter Gerstoft and Haibin Wang and Zhenglin Li},
journal= {arXiv preprint arXiv:1903.12319},
year = {2019}
}
Comments
It has been published on the Journal of the Acoustical Society of America