Adaptive unscented Kalman filter for parameter and state estimation of nonlinear high-speed objects

被引：31
|
作者：
Deng, Fang [1 ,2 ]
Chen, Jie [1 ,2 ]
Chen, Chen [1 ,2 ]
机构：
[1] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
[2] Key Lab Intelligent Control & Decis Complex Syst, Beijing 100081, Peoples R China
来源：
JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS | 2013年 / 24卷 / 04期
基金：
中国国家自然科学基金; 美国国家科学基金会;
关键词：
parameter estimation; state estimation; unscented Kalman filter (UKF); strong tracking filter; wavelet transform; SYSTEMS; ROBUST; NOISE;
D O I：
10.1109/JSEE.2013.00076
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An adaptive unscented Kalman filter (AUKF) and an augmented state method are employed to estimate the time-varying parameters and states of a kind of nonlinear high-speed objects. A strong tracking filter is employed to improve the tracking ability and robustness of unscented Kalman filter (UKF) when the process noise is inaccuracy, and wavelet transform is used to improve the estimate accuracy by the variance of measurement noise. An augmented square-root framework is utilized to improve the numerical stability and accuracy of UKF. Monte Carlo simulations and applications in the rapid trajectory estimation of hypersonic artillery shells confirm the effectiveness of the proposed method.
引用
收藏
页码：655 / 665
页数：11
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