UAV Communication Signal Recognition: A New Feature Representation and Deep-Learning Method

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dc.contributor.authorLi, Lin
dc.contributor.authorDong, Zhiyuan
dc.contributor.authorYu, Xiaorui
dc.contributor.authorRen, Zhiyuan
dc.contributor.authorZhu, Zhigang
dc.contributor.authorJiang, Li
dc.coverage.issue4cs
dc.coverage.volume30cs
dc.date.accessioned2021-12-13T09:46:53Z
dc.date.available2021-12-13T09:46:53Z
dc.date.issued2021-12cs
dc.description.abstractAs the threats from unmanned aerial vehicles (UAVs) increases gradually, to recognize and classify unknown UAVs have became more and more important in both civil and military security fields. Classification of signal modulation types is one of the basic techniques for specific UAV recognition. In this paper, to represent the hidden features involved in the transmitted signals from UAVs, we propose a two-dimensional squeezing transform (TDST) to characterize the UAV communication signals in a compressed time-frequency plane. The new time-frequency representation, TDST, retains the instantaneous characteristics of the UAV signal, and is with excellent data reduction and noise suppression capabilities. The TDST plane of different modulation types are then considered as input features, and we propose a convolutional neural network (CNN) based on deep-learning to recognize the UAV signals. We design an interception system and consider 10 types of UAV signals with random initial phase, bandwidth and frequency offset. Experimental results demonstrate the effectiveness and superiority of the proposed algorithm.en
dc.formattextcs
dc.format.extent713-718cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2021 vol. 30, č. 4, s. 713-718. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2021.0713en
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/203179
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2021/21_04_0713_0718.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectAutomatic modulation classificationen
dc.subjectunmanned aerial vehiclesen
dc.subjectsqueezing transformen
dc.subjectconvolutional neural networken
dc.titleUAV Communication Signal Recognition: A New Feature Representation and Deep-Learning Methoden
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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