物联网学报 ›› 2020, Vol. 4 ›› Issue (3): 60-68.doi: 10.11959/j.issn.2096-3750.2020.00181

• 专题:智慧交通物联网 • 上一篇    下一篇

多源异构航班航迹数据流实时融合方法研究

张瞩熹1,2,田旺1,3,朱少川1,4,刘洪岩1,4,朱熙1,5()   

  1. 1 北京航空航天大学综合交通大数据应用技术国家工程实验室,北京 100083
    2 中国人民解放军32751单位,北京 100039
    3 北京航空航天大学大型飞机高级人才培训班,北京 100083
    4 北京航空航天大学电子信息工程学院,北京 100083
    5 北京航空航天大学前沿科学技术创新研究院,北京 100083
  • 修回日期:2020-07-31 出版日期:2020-09-30 发布日期:2020-09-07
  • 作者简介:张瞩熹,男,博士,主要研究方向为综合交通信息化及大数据技术、航空交通数据挖掘分析等|田旺,男,北京航空航天大学硕士生,主要研究方向为交通大数据、航迹融合等|朱少川,男,北京航空航天大学博士生,主要研究方向为机器学习、航迹模式挖掘、航迹预测等|刘洪岩,男,北京航空航天大学硕士生,主要研究方向为交通大数据、空域态势计算与调控等|朱熙,男,博士,北京航空航天大学助理研究员、硕士生导师,主要研究方向为交通大数据、空域态势计算与调控等
  • 基金资助:
    国家重点研发计划(2019YFF0301400);国家自然科学基金资助项目(61722102);国家自然科学基金资助项目(61671031);国家自然科学基金资助项目(61961146005)

Research on real-time fusion method of multi-source heterogeneous flight trajectory data stream

Zhuxi ZHANG1,2,Wang TIAN1,3,Shaochuan ZHU1,4,Hongyan LIU1,4,Xi ZHU1,5()   

  1. 1 National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation,Beihang University,Beijing 100083,China
    2 Unit 32751,Chinese People’s Liberation Army,Beijing 100039,China
    3 Large Aircraft Advanced Training Center,Beihang University,Beijing 100083,China
    4 School of Electronic and Information Engineering,Beihang University,Beijing 100083,China
    4 Research Institute of Frontier Science,Beihang University,Beijing 100083,China
  • Revised:2020-07-31 Online:2020-09-30 Published:2020-09-07
  • Supported by:
    The National Key R&D Program of China(2019YFF0301400);The National Natural Science Foundation of China(61722102);The National Natural Science Foundation of China(61671031);The National Natural Science Foundation of China(61961146005)

摘要:

二次雷达和广播式自动相关监视(ADS-B,automatic dependent surveillance-broadcast)是在空域监视系统中共存的两种主要监视手段,为了提高监视的精度和稳定性,实现二次雷达和 ADS-B 航迹实时融合至关重要。针对现有方法难以满足大规模航迹的实时融合需求,设计了一种使用大数据技术的二次雷达与 ADS-B 数据流实时融合的方法。该方法基于微批处理的大数据处理框架,遵循MapReduce编程模型,在得到较高质量融合航迹的同时,保障了系统数据处理的高并发能力与实时性。最后,基于真实航班数据开展了航迹实时融合仿真实验,验证了方法的可行性。

关键词: 航迹融合, 多源异构, 微批处理, MapReduce, 流式大数据

Abstract:

Secondary surveillance radar (SSR) and automatic dependent surveillance-broadcast (ADS-B) are the two main surveillance methods coexisting in the airspace surveillance system.In order to improve the accuracy and stability of surveillance,real-time fusion of SSR and ADS-B trajectory is crucial.In view of the fact that the existing methods are difficult to meet the real-time fusion requirements of large-scale trajectories,a real-time fusion method of SSR and ADS-B data streams was designed with big data technology.This method was based on the big data processing framework of micro-batch processing and followed the MapReduce programming model.While obtaining a fusion trajectory of high quality,it ensured high concurrency and real-time data processing capability of the system.Finally,a real-time flight simulation experiment based on real flight data was carried out to verify the feasibility of the method.

Key words: trajectory fusion, multi-source heterogeneous, micro-batch processing, MapReduce, streaming big data

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