ACAS sXu: Robust Decentralized Detect and Avoid for Small Unmanned Aircraft Systems

被引：28

作者：

Alvarez, Luis E. ^{[1
]}

Jessen, Ian ^{[1
]}

Owen, Michael P. ^{[1
]}

Silbermann, Joshua ^{[2
]}

Wood, Paul ^{[2
]}

机构：

[1] MIT, Lincoln Lab, Lexington, MA 02420 USA

[2] Johns Hopkins Univ, Appl Phys Lab, Baltimore, MD 21218 USA

来源：

2019 IEEE/AIAA 38TH DIGITAL AVIONICS SYSTEMS CONFERENCE (DASC) | 2019年

关键词：

Machine Learning; Collision Avoidance; Detect and Avoid; sUAS; UTM;

D O I：

10.1109/dasc43569.2019.9081631

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

Demand for small unmanned aircraft systems (sUAS) continues to increase in diversity and volume, however applications are currently limited by regulatory requirements for visual observation or special use waivers. For full integration of autonomous sUAS in the national airspace system, a collision avoidance system must be implemented to enable detection and avoidance of air traffic. Building upon collision avoidance systems development over the past decade for large UAS and manned aircraft, ACAS sXu provides such a capability, enabling autonomous and decentralized sUAS collision avoidance capability against manned aircraft, UAS, and other sUAS.

引用

页数：9

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