Depth Discrimination for Low-Frequency Sources Using a Horizontal Line Array of Acoustic Vector Sensors Based on Mode
Guolong Liang1,2,3, Yifeng Zhang4,5,6, Guangpu Zhang7,8,9
1Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China. liangguolong@hrbeu.edu.cn.
Sensors (Basel, Switzerland)
|November 2, 2018
Summary
This study introduces a novel depth discrimination method for underwater acoustic sources using a horizontal line array of vector sensors. The technique enhances target classification by analyzing modal amplitudes, offering improved mobility over traditional vertical arrays.
Area of Science:
- Acoustics
- Signal Processing
- Oceanography
Background:
- Depth discrimination is crucial for underwater acoustic detection and target classification of low-frequency sources.
- Conventional methods using vertical line arrays suffer from poor mobility due to their size.
Purpose of the Study:
- To propose a new depth-discrimination method for low-frequency underwater sources.
- To utilize a horizontal line array (HLA) of acoustic vector sensors and mode extraction for improved performance and mobility.
Main Methods:
- Establishing linear equations for modal amplitudes via modal beamforming in vector mode space.
- Solving equations using the total least square algorithm to estimate modal amplitudes.
- Using the power percentage of low-order modes as a decision metric and constructing robust testing hypotheses.
Main Results:
- Vector sensors offer improved depth discrimination compared to scalar sensors due to appropriate mode weighting.
- The method maintains good performance with a relatively short HLA.
- The constructed hypotheses demonstrate high robustness against mismatched environments.
Conclusions:
- The proposed method effectively discriminates the depth of low-frequency underwater sources using an HLA of vector sensors.
- The technique offers enhanced mobility and robustness, suitable for various acoustic environments except specific winter waveguides.
- Further validation through robustness analysis and simulations confirms the method's effectiveness.
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