Multi-sensor Data Fusion for UAV Landing Guidance Based on Bayes Estimation

Abstract

The safety issues in the UAV landing process have recently attracted widespread attention. This paper proposes a multi-sensor data fusion algorithm based on Bayes estimation to achieve precise positioning during the autonomous landing of the drone. This method uses outlier detection, state estimation, and data fusion to analyze and process measurement data from multiple sensors in real time to obtain the best real-time data during the autonomous landing of the drone. The simulation results show that this algorithm has good accuracy and robustness in solving the landing guidance problem, can initially achieve autonomous landing guidance for drones, and also has important reference value for the future realization of carrier-based aircraft autonomous landing and fighter precise guidance.