A Modified Kalman Filtering via Fuzzy Logic System for ARVs Location

Abstract

This paper presents a method for sensor fusion based on adaptive fuzzy Kalman filtering. The method is applied in fusing position signals from Global Positioning System (GPS) and inertial navigation system (INS) for autonomous robot vehicles (ARVs). The noise covariance of Kalman filter (KF) is modified on-line by the fuzzy adaptive controller in order to modulate Kalman filtering to be optimal and to improve the positioning accuracy of the integrated navigation system. The noise controller is based on fuzzy inference system (FIS), and compared with the performance of a simple Kalman filter (SKF). It is demonstrated that the FIS Kalman filtering gives better results, in terms of accuracy, than the SKF.