Guidance law for a surveillance UAV swarm tracking a high capability malicious UAV

Brown, Jason ORCID: https://orcid.org/0000-0002-0698-5758 and Raj, Nawin (2021) Guidance law for a surveillance UAV swarm tracking a high capability malicious UAV. In: 2021 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob 2021), 8-10 Apr 2021, Bandung, Indonesia.

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Abstract

Significant research is currently focused on the issue of malicious UAVs or drones disrupting critical services (e.g. civilian airport operations). One mitigation is to track or pursue a malicious UAV back to its point of origin (and possibly its owner) using a swarm of surveillance UAVs. This becomes of particular academic interest when the malicious UAV has superior capabilities to the individual surveillance UAVs (e.g. in terms of maximum speed). In this paper, we deduce a guidance law (i.e. a rule for determining the direction of flight) for individual surveillance UAVs to maximize the tracking time of a highly capable malicious UAV. We then demonstrate the validity of the analysis using some examples with realistic contemporary UAV capability parameters. The significance of this research is that, in a networked swarm of surveillance UAVs which communicate with each other, if each surveillance UAV maximizes its tracking time, there is a higher probability that the next closest surveillance UAV can be in a position to assume tracking responsibilities when the malicious UAV moves out of tracking range of the original surveillance UAV. In order to demonstrate this, a simulation of a networked swarm of surveillance UAVs which track a high capability malicious UAV is undertaken under various scenarios.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Virtual conference. Accepted version deposited in accordance with the copyright policy of the publisher.
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: 31 May 2021 05:14
Last Modified: 01 Jun 2021 03:08
Uncontrolled Keywords: UAV, drone, networking, tracking, pursuit, guidance
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
A Defence > 81 Defence > 8101 Defence > 810104 Emerging Defence Technologies
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/APWiMob51111.2021.9435240
URI: http://eprints.usq.edu.au/id/eprint/42090

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