Advances in International Computer Science

This paper delves into the crucial problem of state estimation for drones and proposes a state estimation method based on the Kalman filter, which is subsequently applied to the flight control and navigation system of Unmanned Aerial Vehicles (UAVs). Initially, a comprehensive introduction to the principles and mathematical model of the Kalman filter algorithm is provided, followed by a discussion of its applicability in UAV state estimation. Subsequently, the design and implementation of a UAV state estimation system based on the Kalman filter are presented, and its performance and effectiveness are verified using sensor data collected during real aircraft operations. The results demonstrate that the Kalman filter-based state estimation method can effectively fuse sensor information, mitigating the impact of sensor noise and model errors on state estimation, thereby significantly improving the accuracy and stability of UAV state estimation.

UAV; State Estimation; Kalman Filte