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Robust high-resolution direction-of-arrival estimation method using DenseBlock-based U-net.

Dajun Sun¹, Zixuan Jia¹, Tingting Teng¹

¹College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China.

The Journal of the Acoustical Society of America

|June 1, 2022

Summary

This summary is machine-generated.

A novel DenseBlock-based U-net improves underwater direction-of-arrival (DOA) estimation by cleaning bearing-time records (BTRs). This data-driven method offers high resolution, generalization, and robustness to array imperfections.

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Area of Science:

Signal Processing
Machine Learning
Underwater Acoustics

Background:

Direction-of-arrival (DOA) estimation is crucial for underwater detection and localization.
Conventional beamforming methods often suffer from sidelobe interference and noise, limiting resolution.
Existing algorithms may require extensive training data or rely on strong prior assumptions about array models.

Purpose of the Study:

To propose a high-resolution DOA estimation method using a DenseBlock-based U-net architecture.
To enhance the clarity of bearing-time records (BTRs) by mitigating sidelobes and noise.
To develop a robust and generalizable DOA estimation technique applicable to real-world underwater scenarios.

Main Methods:

A U-net architecture incorporating DenseBlocks was developed, replacing standard convolutional layers.
The network was trained to process conventional beamforming BTRs, learning to suppress interference.
The method was validated using both simulated and experimental underwater acoustic data.

Main Results:

The proposed DenseBlock-U-net effectively removed sidelobes and noise, yielding cleaner BTRs with narrow beam widths.
The data-driven approach demonstrated high generalization capabilities, performing well on actual data after training on simulations.
The network efficiently handled multi-target scenarios without requiring specific training for varying numbers of targets, reducing training set size.
The method exhibited superior robustness to array imperfections compared to traditional model-based DOA algorithms.

Conclusions:

The DenseBlock-based U-net presents a powerful and effective solution for high-resolution underwater DOA estimation.
This approach offers significant advantages in terms of resolution, noise/sidelobe suppression, generalization, and robustness.
The method's data-driven nature and efficiency make it a promising advancement for underwater acoustic signal processing.