Predictive Tracking of a High Capability Malicious UAV

Brown, Jason and Raj, Nawin (2021) Predictive Tracking of a High Capability Malicious UAV. In: IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC 2021), 27-30 Jan 2021, 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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Date Deposited: 18 Mar 2021 03:40
Last Modified: 18 Mar 2021 03:40
Uncontrolled Keywords: UAV, drone, communication, predictive tracking
Fields of Research (2008): 08 Information and Computing Sciences > 0805 Distributed Computing > 080503 Networking and Communications
09 Engineering > 0913 Mechanical Engineering > 091303 Autonomous Vehicles
09 Engineering > 0913 Mechanical Engineering > 091302 Automation and Control Engineering
10 Technology > 1005 Communications Technologies > 100510 Wireless Communications
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4606 Distributed computing and systems software > 460609 Networking and communications
40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400702 Automation engineering
40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400703 Autonomous vehicle systems
40 ENGINEERING > 4006 Communications engineering > 400608 Wireless communication systems and technologies (incl. microwave and millimetrewave)
Socio-Economic Objectives (2008): A Defence > 81 Defence > 8101 Defence > 810107 National Security
B Economic Development > 89 Information and Communication Services > 8901 Communication Networks and Services > 890103 Mobile Data Networks and Services
Socio-Economic Objectives (2020): 14 DEFENCE > 1401 Defence > 140104 Emerging defence technologies
14 DEFENCE > 1401 Defence > 140109 National security
22 INFORMATION AND COMMUNICATION SERVICES > 2201 Communication technologies, systems and services > 220107 Wireless technologies, networks and services
Identification Number or DOI: https://doi.org/10.1109/CCWC51732.2021.9376137
URI: http://eprints.usq.edu.au/id/eprint/41576

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