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dc.contributor.authorRahman, Samiur
dc.contributor.authorRobertson, Duncan A.
dc.contributor.editorRanney, Kenneth I.
dc.contributor.editorRaynal, Ann M.
dc.date.accessioned2020-06-29T12:30:11Z
dc.date.available2020-06-29T12:30:11Z
dc.date.issued2020-04-23
dc.identifier.citationRahman , S & Robertson , D A 2020 , Multiple drone classification using millimeter-wave CW radar micro-Doppler data . in K I Ranney & A M Raynal (eds) , Radar Sensor Technology XXIV . , 1140809 , Proceedings of SPIE , vol. 11408 , SPIE , SPIE Defense + Commercial Sensing , 27/04/20 . https://doi.org/10.1117/12.2558435en
dc.identifier.citationconferenceen
dc.identifier.isbn9781510635937
dc.identifier.isbn9781510635944
dc.identifier.issn0277-786X
dc.identifier.otherPURE: 268563786
dc.identifier.otherPURE UUID: 21686ce6-6470-4d8e-89c0-2c5d0b5b7dd0
dc.identifier.otherRIS: urn:1A2755B78E5991D6937086853D94EED6
dc.identifier.otherORCID: /0000-0002-5477-4218/work/74873079
dc.identifier.otherORCID: /0000-0002-4042-2772/work/74873207
dc.identifier.urihttp://hdl.handle.net/10023/20171
dc.descriptionFunding: Army Research Laboratory under Cooperative Agreement Number: W911NF-19-2-0075.en
dc.description.abstractThis paper investigates the prospect of classifying different types of rotary wing drones using radar. The proposed method is based on the hypothesis that the rotor blades of different sizes and shapes will exhibit distinct Doppler features. When sampled unambiguously, these features can be properly extracted and then can be used for classification. We investigate various continuous wave (CW) spectrogram features of different drones obtained with a low phase noise, coherent radar operating at 94 GHz. Two quadcopters of different sizes (DJI Phantom Standard 3 and Joyance JT5L-404) and a hexacopter (DJI S900) have been used during the experimental trial for data collection. For classification training, we first show the limitation of the feature extraction based method. We then propose a convolutional neural network (CNN) based approach in which the classification training is done by using micro-Doppler spectrogram images. We have created an extensive dataset of spectrogram images for classification training, which have been fed to the existing GoogLeNet model. The trained model then has been tested with unseen and unlabelled data for performance verification. Validation accuracy of above 99% is achieved along with very accurate testing results, demonstrating the potential of using neural networks for multiple drone classification.
dc.format.extent9
dc.language.isoeng
dc.publisherSPIE
dc.relation.ispartofRadar Sensor Technology XXIVen
dc.relation.ispartofseriesProceedings of SPIEen
dc.rightsCopyright © 2020 SPIE. 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 https://doi.org/10.1117/12.2558435.en
dc.subjectMicro-Doppleren
dc.subjectRadaren
dc.subjectCWen
dc.subjectMillimeter waveen
dc.subjectClassificationen
dc.subjectDronesen
dc.subjectDeep learningen
dc.subjectNeural networken
dc.subjectGoogLeNeten
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQC Physicsen
dc.subjectT Technologyen
dc.subject.lccQA75en
dc.subject.lccQCen
dc.subject.lccTen
dc.titleMultiple drone classification using millimeter-wave CW radar micro-Doppler dataen
dc.typeConference itemen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Physics and Astronomyen
dc.identifier.doihttps://doi.org/10.1117/12.2558435


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