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Correction physics-informed neural network-aided matched field processing technique for underwater passive source

Hongbo Miao^1,2,3, Li Li^1,2,3,4, Yuxiang Zhang^1,2,3

¹National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China.

The Journal of the Acoustical Society of America

|July 9, 2025

Summary

This summary is machine-generated.

A new method, correction physics-informed neural network-aided matched field processing (CrPIMFP), improves underwater passive source ranging by correcting environmental mismatches with minimal data. This robust technique enhances accuracy in challenging shallow water conditions.

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Area of Science:

Acoustics
Machine Learning
Signal Processing

Background:

Matched field processing (MFP) is crucial for underwater passive source ranging.
Environmental mismatch, particularly with replica generation, poses a significant challenge to MFP accuracy.

Purpose of the Study:

To propose a data-efficient and physics-conforming ranging method, correction physics-informed neural network-aided MFP (CrPIMFP).
To mitigate replica environmental mismatch in underwater passive source ranging.

Main Methods:

Developed a correction physics-informed neural network (CrPINN) to correct acoustic propagation model replicas using minimal measured data.
Integrated normalized correction loss, Helmholtz equation, and boundary conditions into the CrPINN loss function.
Enabled replica interpolation to unsampled points, reducing reliance on labeled data.

Main Results:

CrPIMFP demonstrated increased resistance to bottom sediment depth/sound speed mismatches and range-dependent environments compared to traditional MFP.
Experimental results in the SWellEx-96 environment confirmed CrPIMFP's superior performance across various scenarios, including limited training data, generalization, sparse arrays, and long distances.

Conclusions:

CrPIMFP significantly reduces the discrepancy between corrected replicas and measured fields, enhancing MFP ranging performance.
The proposed method exhibits robustness and outperforms conventional ranging algorithms, validating its effectiveness in diverse underwater acoustic conditions.