Enhancing feature-aided data association tracking in passive sonar arrays: An advanced Siamese network approach

Yunhao Wang ^1,2, Weihang Nie ^1,2, Ziyuan Liu ^1,2

⁰Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.

The Journal of the Acoustical Society of America +

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February 3, 2025

View abstract on PubMed

Summary

This study introduces BiChannel-SiamDinoNet for improved multi-target tracking in passive sonar arrays. The deep learning approach enhances data association accuracy in challenging marine environments.

Area Of Science

Signal Processing
Machine Learning
Ocean Acoustics

Background

Traditional multi-target tracking in passive sonar arrays relies on kinematic data, which is insufficient in complex marine environments.
Existing feature-aided methods struggle with low signal-to-noise ratios and close-proximity targets due to raw feature utilization.

Purpose Of The Study

To develop an advanced feature-aided data association method for passive sonar multi-target tracking.
To improve tracking accuracy and robustness in challenging underwater acoustic scenarios.

Main Methods

Proposes BiChannel-SiamDinoNet, a Siamese network integrated into a joint probability data association framework.
Utilizes an embedding space for acoustic target features to enhance discrimination.
Refines feature extraction for underwater acoustic signals and employs knowledge distillation for improved feature consistency assessment.

Main Results

The BiChannel-SiamDinoNet demonstrates enhanced robustness to variations and complex target relationships.
The method effectively discriminates between measurements and targets, improving data association.
Performance validated through simulations and marine experiments.

Conclusions

The proposed BiChannel-SiamDinoNet significantly improves feature-aided multi-target tracking for passive sonar arrays.
The deep learning approach offers a robust solution for complex underwater acoustic environments.
This method enhances the reliability of tracking systems in challenging marine scenarios.