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

Ergin Gokkaya; Tayfun Gunel

doi:10.23919/ELECO47770.2019.8990550

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

Ergin Gokkaya
, Tayfun Gunel

Elektronik ve Hab.Müh.Bölümü

Scientific and Technological Research Council of Turkey

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

6 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 dil	İngilizce
Ana bilgisayar yayını başlığı	ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	575-579
Sayfa sayısı	5
ISBN (Elektronik)	9786050112757
DOI'lar	https://doi.org/10.23919/ELECO47770.2019.8990550
Yayın durumu	Yayınlandı - Kas 2019
Etkinlik	11th International Conference on Electrical and Electronics Engineering, ELECO 2019 - Bursa, Turkey Süre: 28 Kas 2019 → 30 Kas 2019

Yayın serisi

Adı	ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	11th International Conference on Electrical and Electronics Engineering, ELECO 2019
Ülke/Bölge	Turkey
Şehir	Bursa
Periyot	28/11/19 → 30/11/19

Bibliyografik not

Publisher Copyright:
© 2019 Chamber of Turkish Electrical Engineers.

Belgeye Erişim

10.23919/ELECO47770.2019.8990550

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Gokkaya, E., & Gunel, T. (2019). A Novel Hybrid Approach for Radar Target Classification Based on SVM and Central Moments with PCA Using RCS. In ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering (pp. 575-579). Makale 8990550 (ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ELECO47770.2019.8990550

Gokkaya, Ergin ; Gunel, Tayfun. / A Novel Hybrid Approach for Radar Target Classification Based on SVM and Central Moments with PCA Using RCS. ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 575-579 (ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering).

@inproceedings{e63d29c35bd2444dae32f44a87163875,

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

abstract = "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.",

author = "Ergin Gokkaya and Tayfun Gunel",

note = "Publisher Copyright: {\textcopyright} 2019 Chamber of Turkish Electrical Engineers.; 11th International Conference on Electrical and Electronics Engineering, ELECO 2019 ; Conference date: 28-11-2019 Through 30-11-2019",

year = "2019",

month = nov,

doi = "10.23919/ELECO47770.2019.8990550",

language = "English",

series = "ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "575--579",

booktitle = "ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering",

address = "United States",

}

Gokkaya, E & Gunel, T 2019, A Novel Hybrid Approach for Radar Target Classification Based on SVM and Central Moments with PCA Using RCS. in ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering., 8990550, ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering, Institute of Electrical and Electronics Engineers Inc., pp. 575-579, 11th International Conference on Electrical and Electronics Engineering, ELECO 2019, Bursa, Turkey, 28/11/19. https://doi.org/10.23919/ELECO47770.2019.8990550

A Novel Hybrid Approach for Radar Target Classification Based on SVM and Central Moments with PCA Using RCS. / Gokkaya, Ergin; Gunel, Tayfun.
ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering. Institute of Electrical and Electronics Engineers Inc., 2019. p. 575-579 8990550 (ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - A Novel Hybrid Approach for Radar Target Classification Based on SVM and Central Moments with PCA Using RCS

AU - Gokkaya, Ergin

AU - Gunel, Tayfun

PY - 2019/11

Y1 - 2019/11

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85080939779

U2 - 10.23919/ELECO47770.2019.8990550

DO - 10.23919/ELECO47770.2019.8990550

M3 - Conference contribution

AN - SCOPUS:85080939779

T3 - ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering

SP - 575

EP - 579

BT - ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 11th International Conference on Electrical and Electronics Engineering, ELECO 2019

Y2 - 28 November 2019 through 30 November 2019

ER -

Gokkaya E, Gunel T. A Novel Hybrid Approach for Radar Target Classification Based on SVM and Central Moments with PCA Using RCS. In ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering. Institute of Electrical and Electronics Engineers Inc. 2019. p. 575-579. 8990550. (ELECO 2019 - 11th International Conference on Electrical and Electronics Engineering). doi: 10.23919/ELECO47770.2019.8990550

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

Özet

Yayın serisi

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Belgeye Erişim

Diğer Dosyalar ve Linkler

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