Sparse Bayesian Learning for Off-Grid DOA Estimation in Alpha Noise

Sparse Bayesian (SBL) method is widely used in DOA estimation recent years. This method can obtain high performance with fewer snapshots. However, it depends on the probability density of noise, which usually assumed to be Gauss distribution. And the performance of this method reduces in alpha noise...

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Bibliographic Details
Main Authors: Wenchao, He, Longkai, Liang, Xuhao, Lv
Format: Conference Proceeding
Language:English
Subjects:
Alpha Noise
Automation
Bayes methods
Closed-form solutions
Computational complexity
Direction-of-arrival estimation
DOA estimation
Estimation
SBL algorithm
Simulation
Sparse Representation
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Summary:Sparse Bayesian (SBL) method is widely used in DOA estimation recent years. This method can obtain high performance with fewer snapshots. However, it depends on the probability density of noise, which usually assumed to be Gauss distribution. And the performance of this method reduces in alpha noise which do not have a probability density of closed-form expression. In this paper, we proposed an algorithm which can deal with this situation using SBL algorithm. To reduce the computational complexity of SBL algorithm, an off-grid method is proposed. Simulation results show that algorithm can obtain high performance in alpha noise efficiently.
ISSN:2688-0938
DOI:10.1109/CAC53003.2021.9727804