Predictive Tracking of a High Capability Malicious UAV

Paper


Brown, Jason and Raj, Nawin. 2021. "Predictive Tracking of a High Capability Malicious UAV." IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC 2021). Las Vegas, United States 27 - 30 Jan 2021 Piscataway, United States. https://doi.org/10.1109/CCWC51732.2021.9376137
Paper/Presentation Title

Predictive Tracking of a High Capability Malicious UAV

Presentation TypePaper
AuthorsBrown, Jason (Author) and Raj, Nawin (Author)
Journal or Proceedings TitleProceedings of 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)
Number of Pages6
Year2021
Place of PublicationPiscataway, United States
ISBN9781665414906
Digital Object Identifier (DOI)https://doi.org/10.1109/CCWC51732.2021.9376137
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/document/9376137
Conference/EventIEEE 11th Annual Computing and Communication Workshop and Conference (CCWC 2021)
Event Details
IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC 2021)
Event Date
27 to end of 30 Jan 2021
Event Location
Las Vegas, United States
Abstract

There is considerable interest in researching methods of deterring, detecting and mitigating the actions of malicious UAVs which can cause service interruption and/or physical damage to civilian infrastructure. One of many methods that have been proposed is to passively track a malicious UAV to its final destination using a swarm of surveillance UAVs. A high capability malicious UAV can outrun any one pursuing UAV, so tracking responsibility must be continually handed over from one pursuing UAV to another in the swarm over time. In this paper, we build on previous research to show how, once a high capability malicious UAV is detected by one member of the swarm of surveillance UAVs, other members of the swarm (which are geographically dispersed) can predictively/proactively move into position to 1) maximize their probability of being able to detect and pursue the malicious UAV at a later time, and 2) maximize their individual tracking times if and when the malicious UAV enters their detection zone. This, of course, requires communication of the current malicious UAV trajectory between networked members of the swarm. A simulation of a sample tracking scenario is presented which quantifies the gain achieved by predictively and dynamically positioning pursuing UAVs to increase the probability that the malicious UAV is within the detection zone of at least one pursuing UAV at any arbitrary time. The gain is significant and ultimately allows a smaller swarm to be deployed for effective tracking.

KeywordsUAV, drone, communication, predictive tracking
ANZSRC Field of Research 2020460609. Networking and communications
400702. Automation engineering
400703. Autonomous vehicle systems
400608. Wireless communication systems and technologies (incl. microwave and millimetrewave)
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Byline AffiliationsSchool of Mechanical and Electrical Engineering
School of Sciences
Institution of OriginUniversity of Southern Queensland
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