多源异构航班航迹数据流实时融合方法研究

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

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。

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.