Set-Membership Based Hybrid Kalman Filter for Nonlinear State Estimation under Systematic Uncertainty

This paper presents a new set-membership based hybrid Kalman filter (SM-HKF) by combining the Kalman filtering (KF) framework with the set-membership concept for nonlinear state estimation under systematic uncertainty consisted of both stochastic error and unknown but bounded (UBB) error. Upon the l...

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Detalles Bibliográficos
Autores principales: Zhao, Yan, Zhang, Jing, Hu, Gaoge, Zhong, Yongmin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038318/
https://www.ncbi.nlm.nih.gov/pubmed/31979194
http://dx.doi.org/10.3390/s20030627

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038318/
https://www.ncbi.nlm.nih.gov/pubmed/31979194
http://dx.doi.org/10.3390/s20030627

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