Off-grid hydroacoustic signal orientation estimation based on interpolation and subspace fitting in coprime arrays
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new sparse Bayesian method for underwater acoustic direction estimation using coprime arrays and signal subspace fitting. The approach enhances accuracy and stability, even in noisy, shallow waters with low signal-to-noise ratios.
Area Of Science
- Acoustics
- Signal Processing
- Array Signal Processing
Background
- Underwater acoustic signal direction estimation is crucial for naval applications.
- Traditional coprime arrays have limitations in utilizing all sensor data.
- Ocean noise in shallow waters degrades the accuracy and resolution of direction estimation.
Purpose Of The Study
- To develop a novel sparse Bayesian approach for direction-of-arrival (DOA) estimation.
- To improve the utilization of hydrophone coprime array information.
- To mitigate the impact of ocean noise on DOA estimation accuracy and resolution.
Main Methods
- Hydroacoustic signals are received using a coprime array.
- Interpolation of virtual array elements fills missing information.
- Atomic norm optimization and covariance matrix reconstruction are used for DOA estimation.
- Signal subspace fitting and Bayesian learning are applied for noise reduction and DOA computation.
Main Results
- Accurate estimation of 12 source angles using subarrays of 5 and 3 elements.
- Effective DOA estimation achieved under low signal-to-noise ratio conditions.
- Demonstrated superior positioning accuracy and algorithm stability compared to existing methods.
Conclusions
- The proposed method effectively utilizes virtual array degrees of freedom.
- Noise interference is significantly reduced, enhancing estimation performance.
- The approach offers a robust solution for underwater acoustic direction estimation.
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