A novel big data architecture in support of ADS-B data analytic

A novel big data architecture in support of ADS-B data analytic
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支持ADS-B数据分析的新型大数据架构

DOI:
10.1109/icnsurv.2015.7121218
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发表时间:
2015
期刊:
2015 Integrated Communication, Navigation and Surveillance Conference (ICNS)
影响因子:
--
通讯作者:
S. Thistlethwaite
S. Thistlethwaite
中科院分区:
--
文献类型:
--
作者:
Erton S. Boci;S. Thistlethwaite

文献摘要

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美国联邦航空管理局(FAA)下一代航空运输系统(NextGen)计划旨在使美国国家空域系统(NAS)现代化,该计划的第一个组成部分是实施广播自动相关监视(ADS-B)地面基础设施。ADS-B程序设计的一个主要方面是地面无线电台基础设施。它确定了美国各地的地面无线电台布局,并对其进行了优化,以满足NAS的系统性能、安全和保障。2014年3月,美国联邦航空局完成了全国范围内的基础设施升级,使空中交通管制员能够更准确、更可靠地跟踪飞机,同时在驾驶舱内为飞行员提供更多信息。超过650个ADS-B无线电与装备的飞机通信,支持新的基于卫星的监视系统。目前,ADS-B系统在以10%的容量运行的情况下,接收处理和存储大型数据集。随着飞机航空电子设备的增加,数据量和存储需求将超出我们现有系统的容量和处理能力。测试了一种新的基于hadoop的架构,可以在几分钟内摄取和分析数十亿个CAT033报告。本文介绍了支持ADS-B大数据量快速分析的“大数据”方法。
The first building block of the Federal Aviation Administration's (FAA) Next Generation Air Transportation System (NextGen) initiative to modernize the US national airspace system (NAS) was the implementation of the Automatic Dependent Surveillance-Broadcast (ADS-B) ground infrastructure. A primary aspect of the ADS-B program design is the terrestrial radio station infrastructure. It determined the terrestrial radio stations layout throughout the US and was optimized to meet system performance, safety and security in the NAS. In March 2014, the FAA completed the nationwide infrastructure upgrade, enabling air traffic controllers to track aircraft with greater accuracy and reliability, while giving pilots more information in the cockpit. More than 650 ADS-B radios communicate with equipped aircraft, supporting the new satellite-based surveillance system. Currently, the ADS-B system ingests processes and stores large data sets, while operating at ten percent capacity. As aircraft avionics equipage increases, the volume of data and storage needs will increase beyond our existing system's capacity and processing capability. A new, Hadoop-based architecture was tested to ingest and analyze billions of CAT033 reports in minutes. This paper presents the “Big Data” approach that was adopted to support fast analytics of large ADS-B data volume.