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 :
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