Sensor fusion based on fuzzy Kalman filtering for autonomous robot vehicle

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.