Direction-of-arrival (DOA) estimation is a fundamental task in array signal processing and is widely used in radar, sonar, wireless communications, and acoustic localization. Although classical methods such as MUSIC and ESPRIT can achieve high resolution under favorable conditions, their performance often degrades in challenging scenarios involving low signal-to-noise ratios, limited snapshots, and closely spaced sources. To address these difficulties, this paper proposes a Bayesian off-grid DOA estimation framework for close-angle and multi-source scenarios. The proposed method combines multi-measurement-vector evidence learning, diversified candidate construction, and multi-start joint continuous-manifold refinement so that multiple plausible close-angle hypotheses can be preserved and further optimized on the exact angular manifold. In this way, the proposed framework alleviates the source merging caused by high steering-vector coherence and improves estimation robustness in challenging conditions. Experimental results under close-angle, well-separated, varying-snapshot, and three-source settings demonstrate that the proposed method achieves competitive and, in many difficult cases, superior estimation accuracy compared with several representative baseline methods, confirming its effectiveness for robust close-angle DOA estimation.
He et al. (Sat,) studied this question.