Multiple model filters applied to wind model estimation for a fixed wing UAV

A. Sharifi, H. Nobahari
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引用次数: 10

Abstract

The flight of unmanned aerial vehicles is often associated with model uncertainties, measurement noises, and environmental disturbances such as wind gust. To mitigate these challenges, the accurate estimation of states is vital. Moreover, the wind model and its parameters should also be estimated and compensated during the flight. In this paper, a multiple model filter is implemented for this purpose. To investigate the performance of the multiple model filter, three different models including constant wind, “1-cosine” model and wind shear model are considered. The multiple model filter utilizes three extended Kalman filter to simultaneously estimate the model of wind, the parameters of the model as well as the current states. Simulation results show that the multiple model filter provides good performance and the wind model is properly estimated. Moreover, small estimation errors, obtained from the simulations, prove the good performance of this approach in estimation of states, wind model, and its parameters.
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多模型滤波器用于固定翼无人机风模型估计
无人机的飞行通常与模型不确定性、测量噪声和环境干扰(如阵风)有关。为了减轻这些挑战,对状态的准确估计至关重要。此外,在飞行过程中还需要对风模型及其参数进行估计和补偿。本文为此实现了一个多模型滤波器。为了研究多模型滤波器的性能,考虑了恒风模型、“1-余弦”模型和风切变模型三种不同模型。多模型滤波器利用三个扩展卡尔曼滤波器同时估计风的模型、模型参数和当前状态。仿真结果表明,该多模型滤波器具有良好的性能,能较好地估计出风的模型。仿真结果表明,该方法对风的状态、风模型及其参数的估计误差较小,具有较好的估计效果。
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