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A Sparse Perspective for Direction-of-Arrival Estimation Under Strong Near-Field Interference Environment
Longhao Qiu1,2,3,4, Tian Lan1,2,3,4, Yilin Wang1,2,3,4
1Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China.
Sensors (Basel, Switzerland)
|January 1, 2020
Summary
This study introduces a new model for direction of arrival (DOA) estimation in passive sonar, effectively handling near-field interference for accurate far-field target detection. The proposed method enhances accuracy and resolution, even with limited data and low signal-to-noise ratios.
Area of Science:
- Signal Processing
- Array Signal Processing
- Sonar Technology
Background:
- Direction of Arrival (DOA) estimation is vital for passive sonar.
- Near-field interference often degrades DOA estimation accuracy for far-field targets.
Purpose of the Study:
- To develop a robust DOA estimation method for far-field targets in the presence of strong near-field interference.
- To improve the accuracy and resolution of DOA estimation under challenging signal conditions.
Main Methods:
- A unified sparse representation model for hybrid far-field and near-field sources.
- A sparse Bayesian framework for spatial spectrum reconstruction.
- An expectation-maximization (EM) algorithm for iterative source number and noise power estimation.
Main Results:
- The proposed algorithm accurately detects and estimates far-field targets by constraining near-field interference.
- Demonstrates higher estimation accuracy and resolution compared to MVDR, MUSIC, and CBF algorithms.
- Effective performance shown under low signal-to-noise ratio (SNR) conditions with limited samples.
Conclusions:
- The developed method offers a significant advancement in passive sonar DOA estimation.
- It provides a reliable solution for scenarios with complex interference patterns.
- Validated through simulations and an experimental case study.
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