Bibliographic Details
| Title: |
Direction-of-Arrival Estimation Based on Variational Bayesian Inference Under Model Errors |
| Authors: |
Can Wang, Kun Guo, Jiarong Zhang, Xiaoying Fu, Hai Liu |
| Source: |
Remote Sensing, Vol 17, Iss 7, p 1319 (2025) |
| Publisher Information: |
MDPI AG, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Science |
| Subject Terms: |
DOA estimation, self-calibration, variational Bayesian inference, non-uniform noise, Science |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2072-4292 |
| Relation: |
https://www.mdpi.com/2072-4292/17/7/1319; https://doaj.org/toc/2072-4292 |
| DOI: |
10.3390/rs17071319 |
| Access URL: |
https://doaj.org/article/0e2cb564932945a08f655ca71b5c1604 |
| Accession Number: |
edsdoj.0e2cb564932945a08f655ca71b5c1604 |
| Database: |
Directory of Open Access Journals |
|
Full text is not displayed to guests. |
Login for full access.
|
