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Direction-of-Arrival Estimation Based on Variational Bayesian Inference Under Model Errors

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Publicado en:Remote Sensing vol. 17, no. 7 (2025), p. 1319
Autor principal: Wang, Can
Otros Autores: Guo, Kun, Zhang, Jiarong, Fu, Xiaoying, Liu, Hai
Publicado:
MDPI AG
Materias:
Sparsity
Lower bounds
Phase deviation
Accuracy
Bayesian analysis
Cramer-Rao bounds
Calibration
Self calibration
Noise sensitivity
Neural networks
White noise
Arrays
Noise
Methods
Phase error
Algorithms
Direction of arrival
Statistical inference
Acceso en línea:Citation/Abstract
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Resumen:The current self-calibration approaches based on sparse Bayesian learning (SBL) demonstrate robust performance under uniform white noise conditions. However, their efficacy degrades significantly in non-uniform noise environments due to acute sensitivity to noise power estimation inaccuracies. To address this limitation, this paper proposes an orientation estimation method based on variational Bayesian inference to combat non-uniform noise and gain/phase error. The gain and phase errors of the array are modeled separately for calibration purposes, with the objective of improving the accuracy of the fit during the iterative process. Subsequently, the noise of each element of the array is characterized via independent Gaussian distributions, and the correlation between the array gain deviation and the noise power is incorporated to enhance the robustness of this method when operating in non-uniform noise environments. Furthermore, the Cramér–Rao Lower Bound (CRLB) under non-uniform noise and gain-phase deviation is presented. Numerical simulations and experimental results are provided to validate the superiority of this proposed method.
ISSN:2072-4292
DOI:10.3390/rs17071319
Fuente:Advanced Technologies & Aerospace Database