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

Abstract

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.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 27 citations exist upstream.