A Novel Hybrid Approach for Radar Target Classification Based on SVM and Central Moments with PCA Using RCS

Ergin Gokkaya, Tayfun Gunel

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

5 Atıf (Scopus)

Özet

Radar cross section values are features which have been frequently used in target classification. The classification performance can be increased by extracting statistical properties of these features. In this paper, central moments are obtained from Radar Cross Section (RCS) values. Next, as a novelty Principal Component Analysis (PCA) is applied to these moments. Then the features extracted in this way are classified by Support Vector Machine (SVM). In order to compare the performance of proposed approach, the results are given according to varying SNR. In order to evaluate the effect of number of eigenvectors, the results are given by changing the number of eigenvector. Finally, the execution times and error performances of the different approaches are compared.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıELECO 2019 - 11th International Conference on Electrical and Electronics Engineering
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
Sayfalar575-579
Sayfa sayısı5
ISBN (Elektronik)9786050112757
DOI'lar
Yayın durumuYayınlandı - Kas 2019
Etkinlik11th International Conference on Electrical and Electronics Engineering, ELECO 2019 - Bursa, Turkey
Süre: 28 Kas 201930 Kas 2019

Yayın serisi

AdıELECO 2019 - 11th International Conference on Electrical and Electronics Engineering

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???event.eventtypes.event.conference???11th International Conference on Electrical and Electronics Engineering, ELECO 2019
Ülke/BölgeTurkey
ŞehirBursa
Periyot28/11/1930/11/19

Bibliyografik not

Publisher Copyright:
© 2019 Chamber of Turkish Electrical Engineers.

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