Intercepting Unmanned Aerial Vehicle Swarms with Neural-Network-Aided Game-Theoretic Target Assignment

dc.creatorMontalbano, Nicholas G.
dc.creatorHumphreys, Todd E.
dc.date.accessioned2020-08-04T17:13:29Z
dc.date.available2020-08-04T17:13:29Z
dc.date.issued2020
dc.description.abstractThis paper examines the use of neural networks to perform low-level control calculations within a larger game-theoretic framework for drone swarm interception. As unmanned aerial vehicles (UAVs) become more capable and less expensive, their malicious use becomes a greater public threat. This paper examines the problem of intercepting rogue UAV swarms by exploiting the underlying game-theoretic nature of large-scale pursuit-evasion games to develop locally optimal profiles for target assignment. It paper also examines computationally efficient means to streamline this process.en_US
dc.description.departmentAerospace Engineeringen_US
dc.identifier.citationNicholas G. Montalbano and Todd E. Humphreys, "Intercepting Unmanned Aerial Vehicle Swarms with Neural-Network-Aided Game-Theoretic Target Assignment," In 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), pp. 36-43. IEEE, 2020.en_US
dc.identifier.urihttps://hdl.handle.net/2152/82446
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/9451
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartofRadionavigation Laboratory Conference Proceedingsen_US
dc.rights.restrictionOpenen_US
dc.subjectpursuit-evasion gameen_US
dc.subjectneural networken_US
dc.subjectUAV swarm controlen_US
dc.titleIntercepting Unmanned Aerial Vehicle Swarms with Neural-Network-Aided Game-Theoretic Target Assignmenten_US
dc.typeConference paperen_US

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