A precise landing method for drones based on UKF and target position constraints in multi-sensor fusion

Abstract To meet the accuracy of navigation and positioning during autonomous landing, this study proposes a multi-sensor fusion path tracking method based on the Unscented Kalman Filter(UKF) and introduces the forced constraint of the target position to enhance navigation accuracy. By building the fusion model of Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) and visual navigation, UKF is used to handle non-linear problems and integrate multi-source sensor data, and the accuracy of positioning is improved. The study takes the target position as a mandatory constraint and adjusts the state update of UKF to ensure that the UAV maintains high-precision path tracking during landing. Simulation and measured data in open environments, complex terrain, and interference conditions show that this method is significantly better than traditional methods in path tracking accuracy, especially in noise interference conditions. Compared to single-sensor navigation, this method significantly reduces path bias and effectively controls errors.