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Related Experiment Videos

Frequency difference multi-branch multi-task learning for underwater source localization in mismatched environments.

Jiati Li1, Bin Wang1, Qihang Xiao1

  • 1College of Information Systems Engineering, Information Engineering University, Zhengzhou 450000, China.

JASA Express Letters
|June 4, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning method using frequency differences to enhance underwater source localization accuracy despite common model mismatches. The approach improves range and depth estimation, even with experimental data.

Related Experiment Videos

Area of Science:

  • Acoustic signal processing
  • Underwater acoustics
  • Machine learning for localization

Background:

  • Model mismatches like array tilt and sound speed profile (SSP) variations degrade underwater source localization performance.
  • Existing model-based deep learning methods are sensitive to these practical environmental and system uncertainties.

Purpose of the Study:

  • To develop a robust deep learning method for underwater source range and depth estimation.
  • To mitigate the performance degradation caused by model mismatches in acoustic localization.

Main Methods:

  • Proposed a frequency-difference-based multi-branch multi-task learning network.
  • Utilized frequency-difference processing to minimize mismatch effects.
  • Employed multi-branch feature fusion to enhance localization accuracy.

Main Results:

  • The network demonstrated strong performance on simulated data with array tilt and SSP mismatches.
  • Successfully generalized to real-world experimental acoustic data.
  • Achieved improved source range and depth estimation accuracy.

Conclusions:

  • The proposed frequency-difference method effectively reduces the impact of model mismatches in underwater localization.
  • This multi-branch multi-task learning approach offers a robust solution for acoustic source localization in complex environments.