Direct position determination of multi-tone acoustic signals using off-grid sparse Bayesian learning in the underwater environment

Wei Wang ^1,2, Shefeng Yan ^1,2, Jirui Yang ^1,2

⁰Institute of Acoustics, Chinese Academy of Sciences, Beijing, 100190, China.

The Journal of the Acoustical Society of America +

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April 17, 2025

View abstract on PubMed

Summary

This study introduces an advanced algorithm for precise underwater target localization using multi-tone acoustic signals. The new method significantly improves positioning accuracy and target resolution compared to existing techniques.

Area Of Science

Acoustics
Signal Processing
Underwater Surveillance

Background

High-precision target localization is essential for underwater surveillance.
Existing direct position determination (DPD) algorithms face limitations in accuracy due to fixed grids and pseudo-target interference.

Purpose Of The Study

To propose an improved DPD algorithm for multi-tone acoustic signals.
To enhance positioning accuracy and multi-target resolution in underwater environments.

Main Methods

Developed an off-grid sparse Bayesian learning-based DPD algorithm (DPD-offGSBL).
Established a unified frequency-domain data model for coherent, incoherent, and mixed signals.
Formulated an off-grid sparse signal representation and explored joint sparsity among arrays.

Main Results

DPD-offGSBL demonstrates superior positioning accuracy and multi-target resolution.
The algorithm approaches the Cramér-Rao bound (CRB) under various conditions.
Validated practical applicability using the SWellEx-96 Experiment Event S5 for single underwater acoustic source localization.

Conclusions

The proposed DPD-offGSBL algorithm offers a significant advancement in underwater acoustic localization.
It effectively addresses limitations of traditional methods, providing higher precision and better resolution.
The algorithm is robust and applicable to real-world underwater surveillance scenarios.