Title :
Data Fusion of MEMS IMU/GPS Integrated System for Autonomous Land Vehicle
Author_Institution :
Dept. of Autom. Control, Beijing Inst. of Technol., Beijing
Abstract :
To improve the performance of MEMES IMU based integrated system, the data fusion method integrated AI and Kalman filter was discussed. First, GPS signal validity and vehicle motion status were identified by using fuzzy logics. And when GPS doesn´t outage, its data were set higher weight to be used to guide ALV; otherwise, AI (artificial intelligence) based integration algorithm with the help of Kalman filter was adopted. The method can make full use of the advantages of KF (Kalman filter) and AI, and reduce their limitations.
Keywords :
Global Positioning System; Kalman filters; artificial intelligence; fuzzy set theory; microrobots; mobile robots; sensor fusion; GPS signal validity; Kalman filter; MEMS IMU-GPS integrated system; artificial intelligence; autonomous land vehicles; data fusion; data fusion method; fuzzy logics; integration algorithm; vehicle motion status; Artificial intelligence; Electromechanical sensors; Global Positioning System; Intelligent sensors; Land vehicles; Micromechanical devices; Navigation; Sensor phenomena and characterization; Sensor systems; Signal processing; ALV; Artificial intelligence; Kalman filter; MEMS IMU/GPS; dead-reckoning;
Conference_Titel :
Information Acquisition, 2006 IEEE International Conference on
Conference_Location :
Shandong
Print_ISBN :
1-4244-0528-9
Electronic_ISBN :
1-4244-0529-7
DOI :
10.1109/ICIA.2006.305846
