An Adaptive Kalman Filter With Inaccurate Noise Covariances in the Presence of Outliers

Autor:	Yongfu Li, Hao Zhu, Guorui Zhang, Henry Leung
Rok vydání:	2022
Předmět:	Noise ComputingMethodologies_PATTERNRECOGNITION Control and Systems Engineering Computer science Bayesian probability Outlier Process (computing) Probability density function State (functional analysis) Kalman filter Electrical and Electronic Engineering Algorithm Computer Science Applications
Zdroj:	IEEE Transactions on Automatic Control. 67:374-381
ISSN:	2334-3303 0018-9286
Popis:	In this paper, a novel variational Bayesian (VB) adaptive Kalman filter with inaccurate nominal process and measurement noise covariances in the presence of outliers is proposed. The probability density functions of state transition and measurement likelihood are modeled as Gaussian-Gamma mixture distributions. The state, process and measurement noise covariances are jointly inferred by the VB technique. Computer simulations show that the proposed method has better filtering accuracy than existing state-of-the-art filters under outlier environments.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0463f841960bd893a873d8b5eb8a1af4 https://doi.org/10.1109/tac.2021.3056343 Zobrazit plný text záznamu