Classification of Ground Targets Based on Radar Micro-Doppler Signatures Using Deep Learning and Conventional Supervised Learning Methods

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dc.contributor.authorCao, Peibei
dc.contributor.authorXia, Weijie
dc.contributor.authorLi, Yi
dc.coverage.issue3cs
dc.coverage.volume27cs
dc.date.accessioned2018-11-23T09:26:52Z
dc.date.available2018-11-23T09:26:52Z
dc.date.issued2018-09cs
dc.description.abstractRadar has great potential in military and civilian areas, including automobile anti-collision, battlefield surveillance, etc., due to its high penetration and all-weather capability. On the basis of traditional targets detection, targets classification can be realized. In this paper, a comparison of targets classification between deep learning (Deep Convolutional Neural Networks (DCNNs)) and conventional supervised learning methods (Support Vector Machine (SVM), Naive Bayes (NB) and SVM-Bayes fusion algorithm) has been made. Furthermore, several factors affecting the accuracy of classifying targets including SNR, decrease of samples, have been researched and discussed. We employ a K-band Doppler radar to acquire the raw signal due to its stationary clutter-rejection, movement detection ability and short wavelength. Then Short-time Fourier Transform (STFT) is applied to the raw signal to characterize micro-Doppler signatures which is the fundament of the classification process. We adopt the DCNNs to deal with the spectrograms directly, while features have been designed and extracted for classification with conventional supervised learning methods. It is shown that the DCNN can achieve average accuracy approximately 99.4% followed by SVM-Bayes fusion algorithm reaching around 95.8%, while the accuracy for SVM and NB is about 94.4% and 91% respectively.en
dc.formattextcs
dc.format.extent835-845cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2018 vol. 27, č. 3, s. 835-845. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2018.0835en
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/137043
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2018/18_03_0835_0845.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectTargets classificationen
dc.subjectmicro-Doppleren
dc.subjectDCNNsen
dc.subjectCW Doppler radaren
dc.subjectSVMen
dc.subjectNaive Bayesen
dc.subjectSVM-Bayes fusionen
dc.titleClassification of Ground Targets Based on Radar Micro-Doppler Signatures Using Deep Learning and Conventional Supervised Learning Methodsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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