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dc.contributor.authorRahman, Samiur
dc.contributor.authorRobertson, Duncan A.
dc.contributor.authorRobertson, Adam M.
dc.contributor.authorGovoni, Mark A.
dc.identifier.citationRahman , S , Robertson , D A , Robertson , A M & Govoni , M A 2021 , Study of radar signatures of drones equipped with threat payloads . in Meetings Proceedings RDP Drone Detectability : Modelling the Relevant Signature . , MP-MSG-SET-183-05 , NATO Science and Technology Organization , NATO Meeting Drone Detectability: Modelling the Relevant Signature , 27/04/21 .
dc.identifier.otherPURE: 276083319
dc.identifier.otherPURE UUID: 26b85fb1-d825-4999-af63-43b2eecf6294
dc.identifier.otherORCID: /0000-0002-4042-2772/work/100549588
dc.identifier.otherORCID: /0000-0002-5477-4218/work/100549735
dc.descriptionThe authors acknowledge the funding received by the Army Research Laboratory under Cooperative Agreement Number: W911NF-19-2-0075.en
dc.description.abstractCommercial or customised drones with the ability to carry payloads have the potential to cause security threats so the need to accurately detect and identify them with suitable sensors has increased in recent times. Radar sensors are well capable of detecting and classifying a drone by using the unique signatures produced from both the stationary and rotating parts of the target. In this study we have examined the radar signatures of drones carrying different types of payloads which simulate the following three hazardous scenarios: 1) liquid spray, 2) Inertial forces simulating a gun recoil effect, and 3) heavy payloads. The main objective was to model the radar signatures of these scenarios and analyse the characteristic signatures. Two radars, operating at 24 GHz and 94 GHz, have been used to collect data to validate the modelling. The results of the study demonstrate that the payloads produce unique radar return signals, mainly in the Doppler domain, which can be used for robust classification.
dc.publisherNATO Science and Technology Organization
dc.relation.ispartofMeetings Proceedings RDP Drone Detectabilityen
dc.rightsCopyright © 2021 the Author(s). This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the final published version of the work, which was originally published at
dc.subjectQC Physicsen
dc.subjectU Military Scienceen
dc.titleStudy of radar signatures of drones equipped with threat payloadsen
dc.typeConference itemen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Physics and Astronomyen

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