Direction of arrival estimation with neural networks via test time self-supervised optimization and array manifold sensinga).
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| Title: | Direction of arrival estimation with neural networks via test time self-supervised optimization and array manifold sensinga). |
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| Authors: | Liu, Yining1,2,3 (AUTHOR), Zhang, Xiaojun4 (AUTHOR), Luo, Ziqiang1,2,3 (AUTHOR), Wang, Zenggang1,2,3 (AUTHOR), Li, Zhenglin1,2,3 (AUTHOR), Xu, Lingji1,2,3 (AUTHOR) xulj26@mail.sysu.edu.cn |
| Source: | Journal of the Acoustical Society of America. May2026, Vol. 159 Issue 5, p3967-3979. 13p. |
| Subjects: | Direction of arrival estimation, Beamforming, Array processing, Machine learning, Signal processing, Artificial neural networks |
| Abstract: | Adaptive beamformers such as the minimum-variance distortionless response (MVDR) are highly sensitive to mismatches in both the sample covariance matrix (SCM) and the array steering vector. This paper proposes a closed-loop Neural-MVDR framework for direction-of-arrival estimation that enforces a distortionless constraint consistent with a physically parameterized array model. The method requires no offline supervised pretraining on external labeled datasets. Instead, it performs per-frame, self-supervised adaptation at test time. For each incoming snapshot, it alternates among steering-vector refinement, robust SCM reconstruction parameterized by a lightweight neural module, and analytical MVDR spectral estimation. Experiments on synthetic data and the SWellEx-96 S5 event demonstrate improved robustness compared with the conventional beamformer, conventional MVDR, and representative robust MVDR variants based on eigenspace suppression, covariance matrix tapering, and oracle-approximating shrinkage, respectively. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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