Adaptive UKF for Target Tracking with Unknown Process Noise Statistics

被引：0
作者：
Shi, Yong [1 ]
Han, Chongzhao [1 ]
Liang, Yongqi [1 ]
机构：
[1] Xi An Jiao Tong Univ, Elect & Informat Engn Dept, Xian, Shaan Xi, Peoples R China
来源：
FUSION: 2009 12TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION, VOLS 1-4 | 2009年
关键词：
Tracking; UKF; modified Sage-Husa estimator; adaptive method; NONLINEAR ESTIMATION; TRANSFORMATION; FILTERS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
With an application to target tracking with unknown process noise, adaptive UKF is presented In this new algorithm, modified Sage-Husa noise statistics estimator is introduced to estimate the system process noise variance adoptively. By estimating the noise covariance online, the proposed method is able to compensate the errors resulting from the change of the noise statistics. Such a mechanism can improve the state estimation accuracy and enlarges its application scope. e simulations show that adaptive UKF can provide better performance in tracking accuracy than the standard UKF, especially in the case of unknown prior system noise statistics.
引用
收藏
页码：1815 / 1820
页数：6
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