| 摘要: |
| 阐述了对无人飞行器进行监控的必要性和重要意义，设计了一种无人飞行器监控数据预处理流程。首先采用空间对准算法将各传感器数据统一到空管系统坐标系下。然后根据无人飞行器的运动特点提出了基于改进“当前”统计模型的自适应卡尔曼目标滤波算法对航迹数据进行平滑滤波，该算法根据滤波新息的变化自适应调整机动频率，仿真表明改进后的算法能够实现对目标更为精确的跟踪。之后对航迹数据中的野值进行了判别和剔除。最后规范统一了各传感器的数据帧格式，实现了对监控数据进行预处理的目的。 |
| 关键词: 无人飞行器 预处理 卡尔曼 野值 |
| DOI：DOI:10.3969/j.issn.1672-2337.2019.03.008 |
| 分类号:TN961 |
| 基金项目: |
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| A Preprocessing Method of Monitoring Data of Unmanned Aerial Vehicle |
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LIU Mingzhong，FENG Jianfeng，MENG Jun，CHEN Junfeng
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Luoyang Electronic Equipment Test Center of China, Jiyuan 454650, China
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| Abstract: |
| This paper expounds the necessity and significance of monitoring unmanned aerial vehicles and designs a process for preprocessing the monitoring data of unmanned aerial vehicles. First, the spatial alignment algorithm is used to unify the data of individual sensors into the air traffic system coordinate system. Then, according to the motion characteristics of unmanned aerial vehicle, the adaptive Kalman filtering algorithm based on improved current statistical model is proposed to smooth the track data. This algorithm can adapt the maneuvering frequency according to the change rate of filter innovation. The simulation shows that the improved algorithm can achieve more accurate tracking of the target. And then, the outliers in the track data are discriminated and rejected. Finally, the data frame formats of individual sensors are unified and the pretreatment of monitoring data is realized. |
| Key words: unmanned aerial vehicle preprocessing Kalman filter outlier |
