State augmentation-based modified iterated extended Kalman filtering

Dianzi Jishu Yingyong

Gao Shanfeng¹  Liu Meihong¹  Li Wei²  Xie Bin³ 

[1] Department of Automation，Shanxi University，Taiyuan 030006，China;Department of Automation，Taiyuan University of Technology，Taiyuan 030024，China;Mitsubishi Heavy Industries Haier(Qingdao)Air-Conditoners Company，LTD.，Qingdao 266200，China;

关键词: state augmentation;    convergence rate;    estimation accuracy;    modified iterated extended kalman filter;   

DOI  :  10.16157/j.issn.0258-7998.190501

来源: DOAJ

【 摘 要 】

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).

【 授权许可】

Unknown