Open AccessMULTI-SENSOR DATA FUSION FOR FUTURE TELEMATICS APPLICATION
Author(s) -
Seong-Baek Kim,
Seung Yong Lee,
Jihoon Choi,
Kyoung-Ho Choi,
Byungtae Jang
Publication year - 2003
Publication title -
journal of astronomy and space sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.273
H-Index - 11
eISSN - 2093-5587
pISSN - 2093-1409
DOI - 10.5140/jass.2003.20.4.359
Subject(s) - global positioning system , computer science , inertial measurement unit , sensor fusion , telematics , real time computing , gnss applications , gps/ins , visibility , assisted gps , kalman filter , position (finance) , trajectory , remote sensing , computer vision , artificial intelligence , telecommunications , geography , physics , finance , astronomy , meteorology , economics
In this paper, we present multi-sensor data fusion for telematics application. Successful telematics can be realized through the integration of navigation and spatial information. The well-determined acquisition of vehicle's position plays a vital role in application service. The development of GPS is used to provide the navigation data, but the performance is limited in areas where poor satellite visibility environment exists. Hence, multi-sensor fusion including IMU (Inertial Measurement Unit), GPS (Global Positioning System), and DMI (Distance Measurement Indicator) is required to provide the vehicle's position to service provider and driver behind the wheel. The multi-sensor fusion is implemented via algorithm based on Kalman Filtering technique. Navigation accuracy can be enhanced using this filtering approach. For the verification of fusion approach, land vehicle test was performed and the results were discussed. Results showed that the horizontal position errors were suppressed around 1 meter level accuracy under simulated Non-GPS availability environment. Under normal GPS environment, the horizontal position errors were under 40 cm in curve trajectory and 27cm in linear trajectory, which are definitely depending on vehicular dynamics