Navigation of unmanned surface vehicle and detection of GPS abnormality by fusing multiple sensor measurements

Abstract

This paper proposes a method for fused navigation of an unmanned surface vehicle (USV). The method also detects the outlier and interference of global positioning system (GPS) simultaneously. The method fuses available sensor measurements through extended Kalman filter (EKF) to find the location and attitude of the USV. The method uses error covariance of estimated location for detection of GPS outlier and interference. When outlier and interference of the GPS is detected, the method excludes GPS data from navigation process. The measurements to be fused for the navigation are position by GPS, acceleration, angular rate, magnetic field, linear velocity, and range and bearing to acoustic beacons. The method is tested using simulated measurement data and measurement data provided by experiment. The results show that the method detects GPS outlier and interference as well as the GPS recovery, which frees the USV navigation from the problem of GPS abnormality.