Hybrid deep neural networks for drone high level intent classification using non-cooperative radar data

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dc.contributor.authorFraser, Benjamin
dc.contributor.authorPerrusquía, Adolfo
dc.contributor.authorPanagiotakopoulos, Dimitrios
dc.contributor.authorGuo, Weisi
dc.date.accessioned2023-09-29T14:37:27Z
dc.date.available2023-09-29T14:37:27Z
dc.date.issued2023-09-22
dc.description.abstractThe proliferation of drones has brought many benefits in different industrial and government sectors due to their low cost and potential applications. Nevertheless, the security and air space can be compromised due to anomalous performances derived to negligence or intentional malicious activities. Thus, identify the hidden intentions of drones’ mission profiles is paramount to execute adequate countermeasures. In this paper, an hybrid deep neural network architecture is proposed to classify the high level intent of drones’ mission profiles using non-cooperative radar. Radar measurements are created synthetically using open access telemetry data of flight trajectories. The proposed architecture exploits the classification and reconstruction capabilities of deep neural models to classify the drones hidden high-level intent. Several experiments and comparisons are carried out to verify the effectiveness of the proposed approach.en_UK
dc.description.sponsorshipRoyal Academy of Engineering and Office of the Chief Science Adviser for National Security under the UK Intelligence Community Postdoctoral Research Fellowship programme.en_UK
dc.identifier.citationFraser B, Perrusquía A, Panagiotakopoulous D, Guo W. (2023) Hybrid deep neural networks for drone high level intent classification using non-cooperative radar data. In: 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 19-21 July 2023, Santa Cruz de Tenerife, Spainen_UK
dc.identifier.eisbn979-8-3503-2297-2
dc.identifier.isbn979-8-3503-2298-9
dc.identifier.urihttps://doi.org/10.1109/ICECCME57830.2023.10252859
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20308
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectdronesen_UK
dc.subjecthigh-level intenten_UK
dc.subjectclassificationen_UK
dc.subjectnovelty detectionen_UK
dc.subjectdeep neural networksen_UK
dc.titleHybrid deep neural networks for drone high level intent classification using non-cooperative radar dataen_UK
dc.typeConference paperen_UK

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