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High-Accuracy Off-Grid Sparse Bayesian Learning with Reliability-Guided Inference for Direction-of-Arrival Estimation
Wenchao He1,2, Haoran Wang2, Hongxi Zhao2
1School of Mechanical and Electrical Engineering, Changchun Humanities and Sciences College, Changchun 130117, China.
Sensors (Basel, Switzerland)
|July 28, 2026
Summary
This study introduces a stabilized off-grid sparse Bayesian learning (SBL) method for direction-of-arrival (DOA) estimation. The novel approach enhances accuracy and stability in complex scenarios, outperforming existing methods.
Area of Science:
- Signal Processing
- Statistical Inference
- Array Signal Processing
Background:
- Off-grid direction-of-arrival (DOA) estimation using sparse Bayesian learning (SBL) faces challenges with posterior relevance statistics, error precision learning, and offset correction.
- These limitations can impact the practical performance of SBL-based DOA estimation methods.
Purpose of the Study:
- To propose a reliability-guided stabilized off-grid SBL method for multisnapshot DOA estimation.
- To enhance the robustness and accuracy of DOA estimation in the presence of off-grid deviations and uncertainties.
Main Methods:
- Developed a method within a first-order multiple-measurement-vector Bayesian model.
- Introduced three stabilization modules: confidence-guided MAP-type shrinkage relevance update, posterior-concentration-guided damped noise update, and trust-region cubic-regularized Newton refinement.
- Utilized reliability-controlled shrinkage, damped noise updates for stable learning, and Newton refinement for bounded off-grid corrections.
Main Results:
- The proposed method demonstrated competitive and stable estimation performance across various challenging scenarios.
- Simulations included off-grid deviation, varying signal-to-noise ratios (SNRs), snapshot numbers, source separations, and random-angle conditions.
- Outperformed representative classical and sparse Bayesian baselines in multisnapshot DOA estimation.
Conclusions:
- The reliability-guided stabilized off-grid SBL method effectively addresses limitations of traditional SBL for DOA estimation.
- The proposed approach offers robust and accurate performance, making it suitable for practical multisnapshot DOA estimation applications.
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