INS/GPS Sensor Fusion based on Adaptive Fuzzy EKF with Sensitivity to Disturbances

Abstract

Abstract The estimation accuracy of the inertial navigation system integrated with the global positioning system (GPS) through multiple kinds of Kalman filters (KFs) has been widely considered. Since the classical KFs could not overcome environmental disturbances and noises, adaptive and robust structures are utilised in sensor fusion techniques. Here, different types of adaptive structures have been assumed. The fuzzy inference system benefits the adaption of the measurement covariance matrix, a scale factor employed to tune the process covariance matrix and the Chi‐square algorithm to detect and bound the disturbances. The estimation accuracy and robustness of the adaptive fuzzy extended Kalman filter (AFEKF) are compared with the unscented Kalman filter (UKF) and extended Kalman filter (EKF) structure in various scenarios involving position, velocity, attitude, accelerometer and gyroscope bias estimation errors. Likewise, in order to evaluate the AFEKF approach practically, an embedded electronic board is designed involving an ARM microcontroller, an inertial measurement unit sensor, and a GPS receiver, which was installed on a land vehicle. The results demonstrated the superiority of the AFEKF over the UKF and EKF in the case of lower estimation error and higher robustness against disturbances and outliers.

Keywords

• sensor fusion
• Global Positioning System
• inertial navigation
• nonlinear filters
• Kalman filters
• microcontrollers