Mitigating Drone Attacks for Large High-Density Events is a Purdue University thesis by Travis L. Cline.
Technological advancements have given rise to the widespread utilization of small unmanned aerial systems (sUAS), commonly called ‘drones.’ The sUAS market is anticipated to expand rapidly, with the FAA projecting approximately 8,000 monthly registrations (FAA, 2019). Notable drone incidents include their involvement in an attack on the Venezuelan president, an undetected landing on the White House grounds, and their use in dropping crude explosives on troops in the Middle East (Gramer, 2017; Grossman, 2018; Wallace & Loffi, 2015). The proliferation rate and high-performance capabilities of these drones have raised significant safety concerns, particularly in densely populated outdoor settings. It’s worth noting that counter-unmanned aerial systems are currently prohibited for all but a select few Federal entities in the U.S., leaving both private and public entities vulnerable.
This exploratory study examines various legal and behavioral interventions for facilities and visitors to mitigate potential casualties during a drone attack. It employs AnyLogic simulation modeling within an amusement park scenario. The data generated from this experiment suggests that implementing behavioral interventions just 30 seconds before a drone attack can reduce casualties by over 55%, and an even greater reduction of up to 62% can be achieved with a 60-second implementation window. Testing also indicates that venue design considerations, such as minimizing sharp corners, covering high-density areas, and creating smoother transitions between areas, can collaboratively contribute to casualty reduction when coupled with a warning system. While the study did show significant casualty mitigation, implementing active threat interception methods would be necessary to design a system capable of preventing casualties altogether.
Publication Date– December 2020
Mitigating Drone Attacks for Large High-Density Events contains the following major sections:
- Literature Review
- Results & Analysis
This paper is available via Creative Commons by 4.0 Deed. There were no changes made to this work by the author. For additional information, please visit CC by 4.0 Deed.
C-UAS Hub does not own this content and provides a link for users at the bottom of the page to access it in its original location. This allows the author(s) to track important article metrics related to their work. All credit goes to its rightful owner.
Author– Travis L. Kline
Post Image Credit: envatoelements by Dobbidodarr