Off-Grid Error Calibration for DOA Estimation Based on Sparse Bayesian Learning

Haosheng Fu

doi:10.1109/TVT.2023.3298965

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Off-Grid Error Calibration for DOA Estimation Based on Sparse Bayesian Learning

2023-12-01

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Haosheng Fu

Fengzhou Dai *

Ling Hong

DOI：10.1109/TVT.2023.3298965

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Abstract

En 中文

Compared with the traditional subspace-based methods, sparse signal recovery (SSR) based methods have obvious advantages in performing the direction of arrival (DOA) estimation of an array, such as working well on the limited number of snapshots, good noise robustness, and so on. However, the occurrence of grid mismatch limits the estimation accuracy of these methods. To solve the problem that the grid mismatch impacts DOA estimation accuracy, this article presents a new method for off-grid DOA estimation using weighted Sinc interpolation, referred to as OGWSISBL. Specifically, the off-grid error is represented as the parameter to be estimated in the observation model. Then, under the sparse Bayes learning (SBL) framework, the off-grid error is calculated using the variational Bayesian expectation maximization (VBEM) method and eliminated by updating the grids with weighted Sinc interpolation. The proposed method can work well in both single and multiple snapshots. Simulation and actual measurement results illustrate that the proposed method is superior to the state-of-the-art methods reported.

Keywords:

Direction of arrival (DOA)

off-grid model

sparse signal recovery

sparse bayesian learning

weighted sinc interpolation