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Enhancing robust acoustic DOA estimation against position errors via fastsparse Bayesian learning
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AI
DOI：10.1016/j.asoc.2024.112499.png)
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摘要
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Advanced methods for acoustic direction-of-arrival (DOA) estimation leverage sparse signal recovery tech-niques, which are valued for providing high-resolution results using a limited number of measurement vectors.Although these technologies are capable, they frequently overlook potential errors in sensor positioning, andtheir computational complexity is excessive, constraining their practical application. This paper introduces anew self-calibrating Bayesian method designed to address this issue. The approach begins by establishing ahierarchical Bayesian model that exploits the sparsity of both signal and position error vectors. Subsequently,a fast algorithm applies a basis selection strategy to efficiently update all variables. Furthermore, the studyalso explores off-grid estimation. The empirical results indicate that our method outperforms state-of-the-artmethods in both accuracy and computational efficiency.
Keyword:
Acoustic DOA estimation
Microphone array
Sparse signal recovery
Position errors
Sparse Bayesian learning
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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