DocumentCode :
3448062
Title :
Multiple Model Kalman Filtering for MEMS-IMU/GPS Integrated Navigation
Author :
Tang Kang-hua ; Mei-Ping, Wu ; Xiao-Ping, Hu
Author_Institution :
Nat. Univ. of Defense Technol., Changsha
fYear :
2007
fDate :
23-25 May 2007
Firstpage :
2062
Lastpage :
2066
Abstract :
The conventional Kalman filtering algorithm requires the definition of a dynamic and stochastic model, and errors of low cost MEMS-IMU are likely to vary temporally. So the conventional Kalman filter exists limitation in MEMS-IMU/GPS integrated navigation. This paper presented the use of multiple model adaptive estimation(MMAE) where multiple Kalman filters were run in parallel using different dynamic or stochastic models in MEMS-IMU/GPS integrated navigation. And the modified multiple model Kalman filter was used in order to solve the limitation of multiple model adaptive estimation(MMAE). Using static tests, the algorithm designed was validated. The test results show that the modified multiple model Kalman filter can improve performance of MEMS-IMU/GPS integrated navigation system, compared to the conventional Kalman filtering algorithm. And using the designed algorithm, the positioning accuracy is better than 5m and velocity accuracy is better than 0.1m/ s2, and the attitude errors are less than 0.5 degrees on the static condition.
Keywords :
Global Positioning System; Kalman filters; adaptive estimation; inertial navigation; micromechanical devices; GPS integrated navigation; MEMS-IMU; dynamic model; multiple model Kalman filtering; multiple model adaptive estimation; stochastic model; Filtering; Global Positioning System; Industrial electronics; Kalman filters; Navigation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics and Applications, 2007. ICIEA 2007. 2nd IEEE Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-0737-8
Electronic_ISBN :
978-1-4244-0737-8
Type :
conf
DOI :
10.1109/ICIEA.2007.4318773
Filename :
4318773
Link To Document :
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