HU Jingsong,LU Huiguo,SHI Jing,et al.Noise Reduction Process of Drone Wind Measurement Data based on Improved Kalman Filter[J].Journal of Chengdu University of Information Technology,2026,41(01):1-6.[doi:10.16836/j.cnki.jcuit.2026.01.001]
基于改进卡尔曼滤波算法的无人机测风数据降噪处理
- Title:
- Noise Reduction Process of Drone Wind Measurement Data based on Improved Kalman Filter
- 文章编号:
- 2096-1618(2026)01-0001-06
- Keywords:
- drone; wind measurement; data noise reduction; adaptive Kalman filter; least squares fitting
- 分类号:
- TN713
- 文献标志码:
- A
- 摘要:
- 无人机搭载微型气象探测系统可实现对大气边界层的直接测量,是细节化气象探测的一种常用方法,在应急气象预报、空气质量监测等方面都有较多应用。实际的探测过程中,由于无人机运动、观测环境变化的影响,采集的数据中可能包含一定的噪声,因此,采用改进的卡尔曼滤波算法对无人机测风数据进行降噪处理,并与传统卡尔曼滤波算法进行对比分析。结果表明,改进卡尔曼滤波算法可有效降低测风数据中的噪声信号,保留风速原始数据更多的特征信息,具有更高的滤波精度,在处理风向数据时有很好的抗野值能力,可有效减小野值引起的风向波动。
- Abstract:
- Unmanned aerial vehicles equipped with miniature meteorological observation systems can directly measure the atmospheric boundary layer, a commonly used equipment for detailed meteorological observation. It has many applications in emergency weather forecasting, air quality monitoring, etc. In the actual observation process, the collected data may contain certain noise due to the influence of the drone's movement and changes in the observation environment. Therefore, this paper uses an improved Kalman filtering algorithm to process the meteorological observation data collected by the drone and compares it with the traditional Kalman filtering algorithm. The results show that the improved Kalman filtering algorithm can effectively suppress the noise in the wind field data and handle outliers in the wind field data. Compared with the traditional Kalman filtering algorithm, it has higher filtering accuracy.
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备注/Memo
收稿日期:2024-06-17
基金项目:国家自然科学基金资助项目(42075129)
通信作者:卢会国.Email:luhuiguo@cuit.edu.cn