A robust state estimation method against GNSS outage for unmanned miniature helicopters

Most unmanned aerial robots use a Global Navigation Satellite System (GNSS), such as GPS, GLONASS, and Galileo, for their navigation. However, from time to time the GNSS fails to function due to geographical restrictions and deliberated jamming. This paper proposes an Unscented Kalman Filter-based GPS/IMU integration method in order to accurately estimate the position and velocity of an unmanned miniature helicopter even when the GNSS malfunctions completely. Different from previous GPS/IMU integration methods that cannot propagate noisy inertial measurements to the position and velocity estimations on the rapid vibratory Vertical Take-Off and Landing (VTOL) platforms during the GNSS outage, this method novelly prioritises the propagations of the states in the Unscented Kalman Filter (UKF) algorithm and leverages the time-varying GNSS dilution of precision in line with the adjustments of the measurement noise covariances. Moreover, this method models the stochastic process in the inertial sensors by the acceleration white noise bias in addition to the commonly used random walking process. Without considering the specific actuation models that vary from vehicle to vehicle, this method can particularly be applied to the quivering unmanned helicopters which equipped with two-stroke engines. It yields a rapid and precise compensation for the sensor errors in order to effectively facilitate the propagations of inertial measurements to the position and velocity estimations. Finally, the superior performance of the proposed method in terms of accuracy and endurance is empirically demonstrated using our fully instrumented JR Voyager GSR helicopter.