摘要
本文针对旋翼无人机状态估计的关键问题展开研究，提出了一种基于卡尔曼滤波的状态估计方法，并将其应用于无人机的飞行控制和导航系统中。首先，详细介绍了卡尔曼滤波算法的原理和数学模型，并探讨了其在无人机状态估计中的适用性。随后，本文设计并实现了一个基于卡尔曼滤波的旋翼无人机状态估计系统，并通过在真实飞机运行时采集到的传感器数据验证了其性能和有效性。结果表明，基于卡尔曼滤波的状态估计方法能够有效地融合传感器信息，减小传感器噪声和模型误差对状态估计的影响，提高了无人机状态估计的准确性和稳定性。
关键词： 旋翼无人机；状态估计；卡尔曼滤波器
Abstract
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.
Key words： UAV; State Estimation; Kalman Filte
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