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Lpi Radar Waveform Classification Using Binary Svm And Multi-Class Svm Based On Principal Components Of Tfi

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dc.contributor.author Bektaş, Almila
dc.contributor.author Ergezer, Halit
dc.date.accessioned 2021-06-17T11:50:54Z
dc.date.available 2021-06-17T11:50:54Z
dc.date.issued 2020
dc.identifier.citation Bektaş, Almila; Ergezer, Halit (2020). "Lpi Radar Waveform Classification Using Binary Svm And Multi-Class Svm Based On Principal Components Of Tfi", Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, Vol. 62, No. 2, pp. 134-152. tr_TR
dc.identifier.issn 1303-6009
dc.identifier.issn 2618-6462
dc.identifier.uri http://hdl.handle.net/20.500.12416/4825
dc.description.abstract Since cognition has become an important topic in Electronic Warfare (EW) systems, Electronic Support Measures (ESM) are used to monitor, intercept and analyse radar signals. Low Probability of Intercept (LPI) radars is preferred to be able to detect targets without being detected by ES systems. Because of their properties as low power, variable frequency, wide bandwidth, LPI Radar waveforms are difficult to intercept with ESM systems. In addition to intercepting, the determination of the waveform types used by the LPI Radars is also very important for applying counter-measures against these radars. In this study, a solution for the LPI Radar waveform recognition is proposed. The solution is based on the training of Support Vector Machine (SVM) after applying Principal Component Analysis (PCA) to the data obtained by Time-Frequency Images (TFI). TFIs are generated using Choi-Williams Distribution. High energy regions on these images are cropped automatically and then resized to obtain uniform data set. To obtain the best result in SVM, the SVM Hyper-Parameters are also optimized. Results are obtained by using one-against-all and one-against-one methods. Better classification performance than those given in the literature have been obtained especially for lower Signal to Noise Ratio (SNR) values. The cross-validated results obtained are compared with the best results in the literature. tr_TR
dc.language.iso eng tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Low Probability of Intercept Radar tr_TR
dc.subject Support Vector Machine tr_TR
dc.subject Principal Component Analysis tr_TR
dc.title Lpi Radar Waveform Classification Using Binary Svm And Multi-Class Svm Based On Principal Components Of Tfi tr_TR
dc.type article tr_TR
dc.relation.journal Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering tr_TR
dc.contributor.authorID 293396 tr_TR
dc.identifier.volume 62 tr_TR
dc.identifier.issue 2 tr_TR
dc.identifier.startpage 134 tr_TR
dc.identifier.endpage 152 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Mekatronik Mühendisliği Bölümü tr_TR


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