Sensor fusion based on fuzzy Kalman filtering for autonomous robot vehicle

Author

Sasiadek, J.Z. ; Wang, Q.

Author_Institution

Dept. of Mech. & Aerosp. Eng., Carleton Univ., Ottawa, Ont., Canada

Volume

4

fYear

1999

fDate

1999

Firstpage

2970

Abstract

Presents a method of sensor fusion based on adaptive fuzzy Kalman filtering. This method has been applied to fuse position signals from the Global Positioning System (GPS) and inertial navigation system (INS) for autonomous mobile vehicles. The presented method has been validated in a 3-D environment and is of particular importance for guidance, navigation, and control of flying vehicles. The extended Kalman filter (EKF) and the noise characteristic have been modified using a fuzzy logic adaptive system and compared with the performance of the regular EKF. It has been demonstrated that the fuzzy adaptive Kalman filter gives better results (more accurate) than the EKF

Keywords

Global Positioning System; adaptive Kalman filters; filtering theory; fuzzy logic; inertial navigation; mobile robots; nonlinear filters; sensor fusion; Global Positioning System; adaptive fuzzy Kalman filtering; autonomous mobile vehicles; autonomous robot vehicle; flying vehicles; fuzzy logic adaptive system; guidance; inertial navigation system; navigation; noise characteristic; Adaptive filters; Filtering; Fuses; Global Positioning System; Inertial navigation; Kalman filters; Mobile robots; Remotely operated vehicles; Robot sensing systems; Sensor fusion;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation, 1999. Proceedings. 1999 IEEE International Conference on

Conference_Location

Detroit, MI

ISSN

1050-4729

Print_ISBN

0-7803-5180-0

Type

conf

DOI

10.1109/ROBOT.1999.774048

Filename

774048