DocumentCode :
3314942
Title :
Design of Adaptive Kalman filter algorithm in integrated navigation system for land vehicles
Author :
Hongsong Du ; Jianhua Cheng ; Bingyu Wang
Author_Institution :
Res. of Inst. of Ships, Navy Acad. of Armament, Beijing, China
fYear :
2013
fDate :
4-7 Aug. 2013
Firstpage :
1492
Lastpage :
1496
Abstract :
Micro inertial navigation system which is based on MEMS (Micro electrical mechanical systems) technology are integrated with other navigation systems (Satellites Navigation systems, Celestial Navigation system) to form integrated navigation systems because of low accuracy. Dynamic noise statistical properties of land vehicles changed with the vehicle motion characteristic continuously, because the land vehicle is affected by wind power and other wicked environments. Adaptive Kalman filter (AKF) is introduced for the sake of improving navigation system´s accuracy in land vehicles. This paper designed a new algorithm based on AKF to enhance the integrated navigation accuracy. Theoretical analysis and induction process are carried out. Computer simulation and practical experiment results demonstrate that the AKF algorithm has higher navigation accuracy than Classical Kalman Filter (CKF). These results also show the algorithm in this paper can realize the navigation for land vehicle effectively.
Keywords :
adaptive Kalman filters; inertial navigation; micromechanical devices; noise; satellite navigation; statistical analysis; vehicles; AKF algorithm; CKF; MEMS technology; adaptive Kalman filter algorithm design; celestial navigation system; classical Kalman filter; computer simulation; dynamic noise statistical properties; induction process; integrated navigation system; land vehicles; microelectrical mechanical system technology; microinertial navigation system; navigation system accuracy; practical experiment; satellite navigation system; theoretical analysis; vehicle motion characteristic; wind power; Estimation; Mathematical model; Navigation; Noise; Noise measurement; Vectors; Vehicles; MEMS technology; adaptive Kalman filter; integrated navigation; noise statistical properties;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Mechatronics and Automation (ICMA), 2013 IEEE International Conference on
Conference_Location :
Takamatsu
Print_ISBN :
978-1-4673-5557-5
Type :
conf
DOI :
10.1109/ICMA.2013.6618134
Filename :
6618134
Link To Document :
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