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Off-Grid Space Alternating Sparse Bayesian Learning
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AI
DOI：10.1109/TIM.2023.3243677.png)
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Abstract
En 中文
Direction of arrival (DOA) and source amplitude estimation are widely encountered in detection and communication as traditional array processing problems. Recently, the sparse Bayesian learning (SBL) technique is increasingly exploited, which outperforms conventional compressive sensing (CS) approaches and provides an improved alternative for DOA and amplitude estimation. Although the accuracy of DOA is enhanced somewhat through SBL, the on-grid implementation of SBL suffers grid mismatch errors due to predefined grids. The off-grid SBL can be employed to address the issue. However, the computation load of off-grid SBL is heavy because of per-iteration matrix inversion and slow convergence, which restricts its application for online systems. An off-grid space alternating multisnapshot SBL (OGSA-MSBL) approach used for DOA and source amplitude estimation is proposed in this article, which introduces the offset between the ground-truth DOA and the grid nodes to refine the model, and employs the space alternating (SA) approach to solve the problem efficiently. Compared with the original SA, the DOA accuracy can be improved even with a reduced number of grids, while the source amplitude can be estimated with higher precision with the first-order Taylor refined beamforming model. As shown by the simulation and experimental results, the recovery accuracy of the proposed method is superior to the state-of-the-art on-grid SBL approaches and comparable to its off-gird counterpart, while much faster in terms of computation efficiency.
Keywords:
Direction-of-arrival estimation
Bayes methods
Array signal processing
Amplitude estimation
Estimation
Covariance matrices
Gamma distribution
Acoustic array processing
beamforming
compressive sensing (CS)
off-grid
sparse Bayesian learning (SBL)
Journal
IF:
5.9
Papers:
1.9W
Citations:
5.8W