4.6 Article

A novel strong tracking cubature Kalman filter and its application in maneuvering target tracking

期刊

CHINESE JOURNAL OF AERONAUTICS
卷 32, 期 11, 页码 2489-2502

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.cja.2019.07.025

关键词

Algorithm time complexity; Cubature Kalman filter; Nonlinear filtering; Robustness; Strong tracking filter

资金

  1. National Natural Science Foundation of China [61573283]

向作者/读者索取更多资源

The fading factor exerts a significant role in the strong tracking idea. However, traditional fading factor introduction method hinders the accuracy and robustness advantages of current strong-tracking-based nonlinear filtering algorithms such as Cubature Kalman Filter (CKF) since traditional fading factor introduction method only considers the first-order Taylor expansion. To this end, a new fading factor idea is suggested and introduced into the strong tracking CKF method. The new fading factor introduction method expanded the number of fading factors from one to two with reselected introduction positions. The relationship between the two fading factors as well as the general calculation method can be derived based on Taylor expansion. Obvious superiority of the newly suggested fading factor introduction method is demonstrated according to different nonlinearity of the measurement function. Equivalent calculation method can also be established while applied to CKF. Theoretical analysis shows that the strong tracking CKF can extract the third-order term information from the residual and thus realize second-order accuracy. After optimizing the strong tracking algorithm process, a Fast Strong Tracking CKF (FSTCKF) is finally established. Two simulation examples show that the novel FSTCKF improves the robustness of traditional CKF while minimizing the algorithm time complexity under various conditions. (C) 2019 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据