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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Vydáno v:International journal of robust and nonlinear control Ročník 35; číslo 8; s. 3297 - 3307
Hlavní autoři: Fan, Yunsheng, Qiao, Shuanghu, Song, Baojian, Wang, Guofeng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken, USA John Wiley & Sons, Inc 25.05.2025
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Témata:
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
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Shrnutí: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 the issue. First, an improved robust Student's t maximum correntropy criterion variational adaptive Kalman filter is proposed for local estimation. The algorithm introduces an adaptive factor in the Student's t variational adaptive Kalman filter algorithm to correct the bias of the error covariance matrix, which improves the estimation accuracy of the algorithm. In addition, an improved kernel width based on the maximum correntropy criterion is employed for modifying the correntropy gain to adjust the filtering gain of the algorithm. Second, an improved adaptive information‐sharing factor is developed to adaptively regulate the fusion weight of the local sensor based on the estimation accuracy of the local filter. Finally, the simulation verifies that the proposed algorithm has higher estimation accuracy than other existing algorithms.
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.7841