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Off-Grid Underwater Acoustic Source Direction-of-Arrival Estimation Method Based on Iterative Empirical Mode
Chuanxi Xing1,2, Guangzhi Tan1,2, Saimeng Dong1,2
1College of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China.
Sensors (Basel, Switzerland)
|September 14, 2024
Summary
This study introduces a new algorithm for direction-of-arrival (DOA) estimation using iterative EMD interval thresholding (EMD-IIT) and off-grid sparse Bayesian learning to improve accuracy in noisy shallow seas.
Area of Science:
- Signal Processing
- Ocean Acoustics
- Array Signal Processing
Background:
- Shallow sea environments present significant ocean noise challenges for hydrophone arrays.
- This noise degrades the accuracy and resolution of target direction-of-arrival (DOA) estimation.
- Existing algorithms struggle with noise interference, impacting performance.
Purpose of the Study:
- To develop a robust DOA estimation algorithm for shallow seas.
- To overcome limitations of conventional methods in noisy conditions.
- To enhance the accuracy and resolution of target orientation estimation.
Main Methods:
- Proposed a novel algorithm combining iterative EMD interval thresholding (EMD-IIT) for denoising and off-grid sparse Bayesian learning for DOA estimation.
- Applied singular value decomposition to the denoised signal to establish an off-grid sparse reconstruction model.
- Utilized Bayesian learning to derive the DOA estimate by maximizing the a posteriori probability of the target signal.
Main Results:
- Achieved 100% resolution probability at an 8° azimuthal separation between adjacent signal sources.
- Maintained 100% resolution probability even at a low signal-to-noise ratio of -9 dB.
- Demonstrated superior performance over conventional MUSIC-like and OGSBI-SVD algorithms in localization accuracy, runtime, and robustness.
Conclusions:
- The proposed EMD-IIT and off-grid sparse Bayesian learning algorithm effectively mitigates ocean noise interference in shallow seas.
- The algorithm significantly improves DOA estimation accuracy and resolution compared to existing methods.
- It offers enhanced robustness and efficiency for underwater target orientation estimation.
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