ADS-B历史飞行轨迹数据清洗方法

王兵

doi:10.19818/j.cnki.1671-1637.2020.04.018

ADS-B历史飞行轨迹数据清洗方法

doi: 10.19818/j.cnki.1671-1637.2020.04.018

王兵¹

1.
南京航空航天大学民航/飞行学院，江苏南京 211106
2.
南京航空航天大学国家空管飞行流量管理技术重点实验室，江苏南京 211106

基金项目:

国家自然科学基金项目 61903187

详细信息

作者简介:
王兵(1979-), 男, 河南洛阳人, 南京航空航天大学讲师, 工学博士, 从事民用航空器轨迹运行研究

中图分类号: V355
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出版历程
- 收稿日期: 2020-02-02
- 刊出日期: 2020-04-25

Data cleaning method of ADS-B historical flight trajectories

WANG Bing¹

1.
College of Civil Aviation/College of Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China
2.
National Key Laboratory of Air Traffic Flow Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China

Funds:

National Natural Science Foundation of China 61903187

More Information

Author Bio:
WANG Bing(1979-), male, lecturer, PhD, evanwb@163.com

摘要

摘要: 为有效解决广播式自动相关监视(ADS-B)历史飞行轨迹数据受地面站分布广度、地形阻挡、电磁干扰等影响而出现的各种字段数据异常情况, 建立了ADS-B数据清洗方法, 并将其分为确定清洗对象、字段去重、异常点清理和时间戳修正这4个步骤; 根据已有样本ADS-B历史数据各字段建立了航迹模型并进行有效性分析, 将时间戳、经度、纬度、气压高度和地速等字段定义为特征字段, 并作为清洗对象; 对ADS-B航迹点序列的时间戳、经度和纬度进行去重, 删除数据重复的相邻航迹点; 为提高清洗效率, 使用有噪声的密度聚类(DBSCAN)方法找出特征字段中的离群点, 并进行异常检测与修正; 为使航迹点状态变化符合质点运动学规律, 使用ADS-B航迹点的经度、纬度、气压高度和地速等字段数据修正时间戳, 并存入已扩展的修正后时间戳字段。研究结果表明: 516个样本航班中有97.58%的异常航迹点被有效识别并清理, 清洗后的航迹点状态更具有渐变性特征; 修正前后的总飞行历时存在10~600 s的差异; 时间戳修正效果主要依赖于地速的准确度, 在实际工程中可根据样本航迹的数据特点有选择地使用时间戳修正值; 建立的ADS-B数据清洗方法可为民用航空工程项目中的飞行轨迹分析、评估与计算等方面提供前期数据处理平台。
- 空中交通 /
- ADS-B飞行轨迹 /
- 数据清洗 /
- DBSCAN方法 /
- 局部遍历 /
- 时间戳修正
Abstract: To effectively solve the various field data anomalies in the automatic dependent surveillance-broadcast(ADS-B) historical flight trajectories affected by the ground station distribution breadth, terrain blocking, electromagnetic interference and so on, an ADS-B data cleaning method was established, and implemented by four steps, such as determining the cleaning object, deleting the duplication of field, cleaning the abnormal point and correcting the time stamp. According to the existing sample ADS-B historical data, the track model was established and the validity was analyzed. The fields such as the time stamp, longitude, latitude, pressure altitude and ground speed were defined as the characteristic fields and cleaning objects. The time stamp, longitude and latitude of ADS-B track point sequence were deduplicated to delete the adjacent track points with repeated data. The outliers of characteristic fields were located through the method based on the density-based spatial clustering of applications with noise(DBSCAN) to improve the cleaning efficiency, detect and correct the abnormality. To make the change of track point state conform to the particle kinematic law, the time stamp was corrected by the field data of longitude, latitude, pressure altitude and ground speed of ADS-B track points, and the extended modified time stamp field was saved. Research result shows that 97.58% of the abnormal track points in the 516 sample flights are effectively identified and cleaned. The cleaned track point state changes more smoothly. The total flight duration before and after correction varies between 10-600 s. The correction effect of time stamp mainly depends on the accuracy of ground speed. The corrected time stamp should be selectively used according to the data characteristics of sample track in practical engineering applications. The established cleaning method of ADS-B data can provide a preliminary data processing platform for the trajectory analysis, evaluation and computing in civil aviation engineering projects.
- air traffic /
- ADS-B flight trajectory /
- data cleaning /
- DBSCAN method /
- local traversal /
- time stamp correction

HTML全文

图 1 样本航班ADS-B经度、纬度、气压高度和垂直速度剖面

Figure 1. Profiles of longitude, latitude, pressure altitude and vertical speed in sample flight

下载: 全尺寸图片幻灯片

图 2 样本航班ADS-B地速/计算地速与航迹角/计算航迹角剖面对比

Figure 2. Profile contrast of ground speed/computed ground speed, track angle/computed track angle in sample flight

下载: 全尺寸图片幻灯片

图 3 时间戳与位置不协调时的修正

Figure 3. Correction of inconformity between time stamp and position

下载: 全尺寸图片幻灯片

图 4 离群点与异常点

Figure 4. Outliers and abnormities

下载: 全尺寸图片幻灯片

图 5 样本航班ADS-B与QAR的3D航迹对比

Figure 5. 3D track contrast between ADS-B and QAR in sample flight

下载: 全尺寸图片幻灯片

图 6 样本ADS-B相邻航迹点飞行历时误差分布

Figure 6. Error distributions of flight durations between adjacent track points in ADS-B sample

下载: 全尺寸图片幻灯片

图 7 相邻航迹点之间的变速运动

Figure 7. Nonuniform motions between adjacent track points

下载: 全尺寸图片幻灯片

图 8 样本ADS-B经时间戳修正后的相邻航迹点飞行历时误差分布

Figure 8. Error distributions of flight durations between adjacent track points after time stamp correction in ADS-B sample

下载: 全尺寸图片幻灯片

图 9 样本ADS-B航迹清洗前后剖面对比

Figure 9. Profile comparison of ADS-B sample track fields before and after data cleaning

下载: 全尺寸图片幻灯片

表 1 特征字段DBSCAN参数配置

Table 1. Parameters configuration in DBSCAN of characteristic fields

特征字段	容许值范围	邻域距离阈值
经度/(°)	[-180, 180]	0.4
纬度/(°)	[-90, 90]	0.2
气压高度/m	[-100, 15 000]	300
地速/(km·h^-1)	[90, 1 350]	200
计算航迹角/(°)		160

下载: 导出CSV

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