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A Fuzzy Strong Tracking Extended Kalman Filter for UAV Navigation Considering Interruption of GPS Signal

For the UAV inertial/GPS integrated navigation system, considering the problem of GPS data interruption in navigation process, this technical paper designs an improved sensors fusion algorithm. Combining the traditional extended Kalman filter (EKF) technology with strong tracking filter, a fuzzy strong tracking extended Kalman filter algorithm is designed by using the membership function of the fuzzy theory. Then the navigation simulation model of UAV is established. The simulation results show that the improved algorithm can quickly adapt to the sudden change of GPS signal, that is, when the GPS signal restores from the fault state to the normal state, the improved algorithm can converge to the stable state more quickly than the EKF algorithm, and complete the estimation of flight state again. At the same time, compared with EKF and strong tracking extended Kalman filter (SKEKF), the improved algorithm in this paper has higher estimation accuracy.