Modified Adaptive Federated Student's t Maximum Correntropy Criterion Variational Adaptive Kalman Filtering for Multi‐Source Data Fusion

ABSTRACT The traditional federated Kalman filter‐based multi‐source data fusion algorithm performs poorly in the presence of outliers and unknown noise, a modified federated robust Student's t maximum correntropy criterion variational adaptive Kalman filter is proposed in this paper to tackle t...

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Bibliographic Details

Published in:	International journal of robust and nonlinear control Vol. 35; no. 8; pp. 3297 - 3307
Main Authors:	Fan, Yunsheng, Qiao, Shuanghu, Song, Baojian, Wang, Guofeng
Format:	Journal Article
Language:	English
Published:	Hoboken, USA John Wiley & Sons, Inc 25.05.2025 Wiley Subscription Services, Inc
Subjects:	Accuracy Adaptive algorithms Algorithms Covariance matrix Criteria Data integration Error correction federated filter information sharing factor Kalman filters maximum correntropy criterion Robustness Simulation student's t distribution variational Bayesian
ISSN:	1049-8923, 1099-1239
Online Access:	Get full text
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