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
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);
Conference_Titel :
Intelligent Control and Automation (WCICA), 2014 11th World Congress on
DOI :
10.1109/WCICA.2014.7053351