Carter, Joshua (2021) Drone Swarm Simulation for Tracking High Capability Malicious Drone. [USQ Project]
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Abstract
The availability of affordable ready-to-fly consumer, commercial, and industrial drones has exploded over the past ten to fifteen years and in turn so have the sales figures. Unprecedented technological advancements in component and material production have largely been credited with the increased affordability and capabilities of consumer drones. Common out-of-the-box features that are standard on many consumer drones present the opportunity for them to be used maliciously. Malicious use cases are wide and range, for example, from illegal surveillance of individuals, to smuggling of contraband into prisons and across borders. Continued breaches of restricted airspace around sensitive sites such as nuclear power plants and airports are especially concerning. Current mitigation methods are insufficient, often failing to identify the drone or its operator, there is a pressing need to track these malicious drones back to their point of origin, without the use of expensive terrestrially based radar systems. Off-the-shelf drones flown in a swarm have been shown to adequately detect and track a malicious drone. The efficacy of swarm detection can be increased through optimal initial flight formations of the swarm.
This research examined a ‘sunflower’ initial flight formation against benchmark formations identified within the literature; this evaluation was carried out through computer simulation. Two tracking methods were simulated for each formation: reactive tracking and reactive tracking with predictive pre-positioning. Multiple simulations for each formation were undertaken using both tracking methods at a variety of swarm sizes. The top speed of the drone swarm was then varied, and the simulations repeated. As the malicious drone is assumed to be of high capability, swarm top speed never exceeded the assumed malicious drone top speed. The output of the simulation was analysed to determine the optimum configuration that would provide the highest proportion of active tracking of the malicious drone while it was within the tracking area.
The simulation results show that the sunflower initial flight formation outperformed the benchmark formations for every configuration under test. As the swarm size increased to values over 500 drones a point of diminishing returns was observable across the board for all formation and tracking strategy configurations at all swarm speeds. Additionally, the performance of the sunflower formation, when coupled with reactive tracking and a more competitive swarm speed, was demonstrated to outperform the benchmark formations even when they had the perceived advantage of predictive pre-positioning.
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| Item Type: | USQ Project |
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| Item Status: | Live Archive |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 - 31 Dec 2021) |
| Supervisors: | Brown, Jason |
| Qualification: | Bachelor of Engineering (Honours) (Electrical and Electronic) |
| Date Deposited: | 02 Jan 2023 23:21 |
| Last Modified: | 26 Jun 2023 00:06 |
| Uncontrolled Keywords: | drone, swarm, simulation, malicious, tracking, prediction |
| URI: | https://sear.unisq.edu.au/id/eprint/51801 |
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