DocumentCode
1585131
Title
Attitude angle aided IMMCKF algorithm
Author
Hai, Chen ; Ganlin, Shan
Author_Institution
Dept. of Opt. & Electron. Eng., Coll. of Mech. Eng., Shijiazhuang, China
Volume
1
fYear
2011
Firstpage
197
Lastpage
200
Abstract
To effectively solve the tracking problem of nonlinear maneuvering target, interacting multiple model cubature Kalman filter (immckf) algorithm brings cubature Kalman filter (ckf) into the interacting multiple model (imm) algorithm. This paper brings the attitude angle information into the immckf algorithm, and identifies the target maneuver mode through the fuzzy association between the attitude angle and the current motion mode of target; then the association result is used to fuse with the model probability of imm to enhance its model resolving power. A simulation of maneuvering target tracking shows that the attitude angle aided immckf (aa-immckf) algorithm can effectively improve the tracking accuracy and stability of the original immckf algorithm.
Keywords
Kalman filters; fuzzy set theory; probability; target tracking; IMMCKF algorithm; attitude angle information; fuzzy association; interacting multiple model cubature Kalman filter; maneuvering target tracking; model probability; nonlinear maneuvering target; target maneuver mode; target motion mode; tracking problem; Computational modeling; Kalman filters; Mathematical model; Probability; Radar tracking; Target tracking; attitude angle; cubature Kalman filter; interacting multiple model; maneuver target tracking;
fLanguage
English
Publisher
ieee
Conference_Titel
Electronic Measurement & Instruments (ICEMI), 2011 10th International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-8158-3
Type
conf
DOI
10.1109/ICEMI.2011.6037712
Filename
6037712
Link To Document