Target detection improvement by using a class decision algorithm for synthetic aperture radar

Mesut Kartal, Sedef Kent, Serdar Kargin

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

Özet

This paper proposes a classification method to improve the target detection accuracy in a synthetic aperture radar processing algorithm. In the imaging algorithm, Fourier based processing algorithm is used to obtain the processed image from the measured 2D Cartesian backscattered frequency domain data. In case of measured data with limited frequency band and aspect angle interval, radar target detection accuracy will be reduced. Besides, the noise in measured data affects the result and thus it is hard to identify the target. In this paper, a decision rule, which is based on a classification algorithm by using neural networks, is proposed to improve the target identification accuracy by comparing the processed image with the images in the data bank. In this work, the effect of noise, frequency and aspect angle limitation in the decision accuracy are investigated and the results are presented. Some other decision methods are also given to compare the results.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıEUSAR 2008 - 7th European Conference on Synthetic Aperture Radar
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9783800730841
Yayın durumuYayınlandı - 2008
Etkinlik7th European Conference on Synthetic Aperture Radar, EUSAR 2008 - Friedrichshafen, Germany
Süre: 2 Haz 20085 Haz 2008

Yayın serisi

AdıProceedings of the European Conference on Synthetic Aperture Radar, EUSAR
Hacim1-4
ISSN (Basılı)2197-4403

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???event.eventtypes.event.conference???7th European Conference on Synthetic Aperture Radar, EUSAR 2008
Ülke/BölgeGermany
ŞehirFriedrichshafen
Periyot2/06/085/06/08

Bibliyografik not

Publisher Copyright:
© 2008 VDE VERLAG GMBH.

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