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Aircraft Classification Using Radar from Small Unmanned Aerial Systems for Scalable Traffic Management Emergency Response OperationsThis work investigates two machine learning techniques: Support Vector Machine (SVM) and Autoencoders (AE)with SVM layer for classification of radar trajectories as General Aviation (GA), fixed-wing small Unmanned Aerial System (sUAS), or not-an-aircraft using radar data recorded from sUAS. Onboard identification of intruder aircraft type is useful for planning avoidance maneuvers and is necessary to provide autonomous systems to meet or exceed the avoidance capability of a human pilot. Aircraft classification can identify intruder aircraft that are not part of the team and may be violating a Temporary Flight Restriction. Aircraft classification is needed in monitoring an airspace where multiple aircraft are teaming on a shared task. Scalable Traffic Management for Emergency Response Operations (STEReO) is a NASA project aimed at improving disaster response by enabling large scale aircraft operations through the teaming of manned aircraft with sUAS to maximize emergency response resources. To this end, this work uses trajectories and radar derived features to classify aircraft from a multirotor sUAS. The AE + SVM generated the strongest classification overall accuracy of 93.5% using the first 4 seconds of radar track data for tracks that activated the avoidance system. Subsampling the available track data increased the available training data with the maximum aircraft recall of 0.94 achieved using the SVM with 1 second track data.
Document ID
20210017749
Acquisition Source
Langley Research Center
Document Type
Conference Paper
Authors
Chester V. Dolph
(Langley Research Center Hampton, Virginia, United States)
George N. Szatkowski
(Langley Research Center Hampton, Virginia, United States)
Henry Holbrook
(Universities Space Research Association Columbia, Maryland, United States)
Chris M. Morris
(Analytical Mechanics Associates (United States) Hampton, Virginia, United States)
Larry A. Ticatch
(TEAMS3)
Mahyar R. Malekpour
(Langley Research Center Hampton, Virginia, United States)
Robert G. McSwain
(Langley Research Center Hampton, Virginia, United States)
Date Acquired
June 18, 2021
Subject Category
Air Transportation And Safety
Meeting Information
Meeting: AIAA AVIATION Forum
Location: Virtual
Country: US
Start Date: August 2, 2021
End Date: August 6, 2021
Sponsors: American Institute of Aeronautics and Astronautics
Funding Number(s)
WBS: 109492.02.07.07.07.06
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
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