@inproceedings{fe122f39887d4d16bfa482303a24cc5d,
title = "Wind Estimation with UAVs Using Improved Adaptive Kalman Filter",
abstract = "This paper presents an improved adaptive Kalman filter algorithm for wind estimation with unmanned aerial vehicles (UAVs). The wind measurement values with UAVs always include large continuous outlier. At the same time, the noise statistical prior knowledge for wind is insufficient. Firstly, the wind state equation including mean wind and turbulence is established by accurate wind model. Then the three-dimensional measurement equation of wind is established based on the principle of the wind measurement by UAVs. Finally, the wind data measured by the UAVs is processed using improved adaptive Kalman filter. Simulation results demonstrate the feasibility of the approach. And compared with the traditional Kalman filter and the adaptive Kalman filter, the proposed algorithm can reduce the influence of large continuous outliers and ensure the accuracy of wind field estimation.",
keywords = "Adaptive Kalman Filter, UAVs, Wind Estimation",
author = "Yaohong Qu and Kai Wang and Xiwei Wu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 31st Chinese Control and Decision Conference, CCDC 2019 ; Conference date: 03-06-2019 Through 05-06-2019",
year = "2019",
month = jun,
doi = "10.1109/CCDC.2019.8832809",
language = "英语",
series = "Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3660--3665",
booktitle = "Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019",
}