A Passive Time Reversal Method with a Metamodel for Underwater Source Localization
A passive time reversal method with a metamodel (PTR-MM) is proposed to improve underwater source localization under ocean conditions. PTR-MM eliminates model mismatch errors by replacing the conventional sound propagation model with a Kriging metamodel. This metamodel is optimally constructed based...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/13/6/1082 |
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| Summary: | A passive time reversal method with a metamodel (PTR-MM) is proposed to improve underwater source localization under ocean conditions. PTR-MM eliminates model mismatch errors by replacing the conventional sound propagation model with a Kriging metamodel. This metamodel is optimally constructed based on measured sound field data. The method combines a metamodel with a passive time reversal (PTR) process to generate a focused sound field whose intensity peaks correspond to source positions. In numerical simulations using the KRAKEN model in a range-independent waveguide, PTR-MM accurately localizes single and multiple sources, is insensitive to mismatches in key environmental parameters, and maintains unbiased performance down to −20 dB signal-to-noise ratios (SNRs). Experimental validation on the SWellEx-96 Event S5 dataset confirms that PTR-MM outperforms conventional PTR in both single- and dual-source localizations, achieving most mean absolute percentage errors (MAPEs) below 10% when trained and tested in consistent environments. Further studies reveal that localization accuracy depends primarily on signal quality, array aperture, and element spacing, rather than on source frequency. However, PTR-MM performance degrades if the metamodel is trained in an environment that differs from the test conditions. The above findings demonstrate the potential of combining PTR with a metamodel for robust and real-time localization. |
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| ISSN: | 2077-1312 |