DocumentCode
3573227
Title
Interacting multiple model algorithm based on S-amended UKF
Author
Zhang Yuan ; Dong Shou-quan ; Yang Xing-bao ; Liu Shu-bo ; Chu Jun-bo
Author_Institution
Coll. of Inf. Sci. & Technol., Dalian Maritime Univ., Dalian, China
fYear
2014
Firstpage
3805
Lastpage
3809
Abstract
Devoted to the problem of maneuvering target tracking under nonlinear observation, an S-amended unscented Kalman filtering (SUKF) is developed in this paper, which uses the idea of S-amended anti-divergent method for Kalman filter in linear filtering and improves the performance of unscented Kalman filtering algorithm. Then based on the coordinated turn (CT) models, adopting the method of SUKF, an interacting multiple model (IMM) algorithm named interacting multiple model algorithm based on S-amended unscented Kalman filtering (IMM-SUKF) is researched. Simulation results show that this algorithm can effectively improve the tracking precision of the multiple model algorithm, especially when the model mismatching and the target´s sudden maneuvering occurs, and that it is suitable for engineering applications, especially for tracking highly maneuvering aerial targets.
Keywords
Kalman filters; nonlinear filters; target tracking; S-amended UKF; S-amended unscented Kalman filtering; coordinated turn models; interacting multiple model algorithm; linear filtering; multiple model algorithm; nonlinear observation; target tracking; Atmospheric modeling; Educational institutions; Filtering algorithms; Kalman filters; Maximum likelihood detection; Target tracking; S-amended; interacting multiple model (IMM); maneuvering target tracking; unscented Kalman filter (UKF);
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation (WCICA), 2014 11th World Congress on
Type
conf
DOI
10.1109/WCICA.2014.7053351
Filename
7053351
Link To Document