多源异构航班航迹数据流实时融合方法研究

doi:10.11959/j.issn.2096-3750.2020.00181

Abstract

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

CLC Number:

V355.1

Zhuxi ZHANG,Wang TIAN,Shaochuan ZHU,Hongyan LIU,Xi ZHU. Research on real-time fusion method of multi-source heterogeneous flight trajectory data stream[J]. Chinese Journal on Internet of Things, 2020, 4(3): 60-68.

Figures/Tables 13

References 26

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呼号	ADS-B记录数/条	二次雷达记录数/条	总处理耗时/s	平均每条数据处理耗时/s
ALK454	1 433	254	28.13	0.016
JJA4304	1 460	239	30.47	0.017
ANA8490	1 342	247	31.52	0.019

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

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