Design of Multi-sensor Fusion Positioning and Navigation Method Based on Federated Kalman Filter for Low-Speed Rotorcraft of Atmospheric Detection

Abstract

Abstract A rotorcraft needs to accurately sample the predetermined path at a low flight rate when performing atmospheric sounding tasks; thus, it is particularly critical to obtain accurate positioning and navigation information in real-time. Hence, this paper designs a multi-sensor fusion positioning and navigation algorithm for atmospheric detection of low-speed rotorcraft. The multi-sensor fusion positioning and navigation algorithm for velocity and spatial position are presented based on the Federated Kalman Filter combined with the accelerometer, Global Positioning System (GPS), and barometer. The experimental results show that the fusion algorithm result of the velocity and spatial position is closer to the actual position than a single sensor. In conclusion, this paper’s multi-sensor fusion positioning and navigation algorithm overcomes the problems of low positioning accuracy of a single sensor and cumulative errors and effectively improves positioning accuracy, real-time, and stability.