State augmentation-based modified iterated extended Kalman filtering

State augmentation-based modified iterated extended Kalman filtering

This paper derives a state augmentation-based modified iterated extended Kalman filter(SMIEKF) by making use of the state augmentation technique, in which the state variables are directly augmented with the measurement noise. The improved performance is then demonstrated by a target tracking problem...

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Bibliographic Details
Main Authors: Liu Meihong, Gao Shanfeng, Li Wei, Xie Bin
Format: Article
Language:zho
Published: National Computer System Engineering Research Institute of China 2020-04-01
Series:Dianzi Jishu Yingyong
Subjects:
state augmentation
convergence rate
estimation accuracy
modified iterated extended kalman filter
Online Access:http://www.chinaaet.com/article/3000117805
Description
Summary:This paper derives a state augmentation-based modified iterated extended Kalman filter(SMIEKF) by making use of the state augmentation technique, in which the state variables are directly augmented with the measurement noise. The improved performance is then demonstrated by a target tracking problem involving radar measurements of an atmospheric re-entry vehicle. Simulation results clearly show that the proposed filtering algorithm exhibits faster convergence rate and better estimate accuracy compared to the modified iterated extended Kalman filter(MIEKF) and traditional extended Kalman filter(EKF).
ISSN:0258-7998

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