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

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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Bibliographic Details
Main Authors: Yan Zhao, Jing Zhang, Gaoge Hu, Yongmin Zhong
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Sensors
Subjects:
nonlinear state estimation
kalman filtering
set-membership
systematic uncertainty
unknown but bounded error
Online Access:https://www.mdpi.com/1424-8220/20/3/627

Internet

https://www.mdpi.com/1424-8220/20/3/627

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