Senem MAKAL, Ahmet KIZILAY

93100

A neural-based electromagnetic inverse scattering approach to the detection of a conducting cylinder coated with a dielectric material

In this study, a radial basis function neural network approach is applied for the estimation of the localization and the radius of a conducting cylinder with a circular cross-section coated with a dielectric material. A set of features, the radar cross-section (RCS) values, are derived from scattered fields, which are calculated using the surface equivalence principle and the method of moment. RCS values are obtained using 10 different scattering angles that are fed into the network. The outputs of the network are the location (x0, y0) and the radius (rp) of the conducting cylinder. This is an application of the electromagnetic inverse scattering of the objects embedded in a material based on the use of a neural network.

Anahtar Kelimeler:

Electromagnetic scattering, the method of moment, neural network, inverse scattering

A neural-based electromagnetic inverse scattering approach to the detection of a conducting cylinder coated with a dielectric material

In this study, a radial basis function neural network approach is applied for the estimation of the localization and the radius of a conducting cylinder with a circular cross-section coated with a dielectric material. A set of features, the radar cross-section (RCS) values, are derived from scattered fields, which are calculated using the surface equivalence principle and the method of moment. RCS values are obtained using 10 different scattering angles that are fed into the network. The outputs of the network are the location (x0, y0) and the radius (rp) of the conducting cylinder. This is an application of the electromagnetic inverse scattering of the objects embedded in a material based on the use of a neural network.

Keywords:

Electromagnetic scattering, the method of moment, neural network, inverse scattering,

Turkish Journal of Electrical Engineering and Computer Science-Cover

ISSN: 1300-0632
Yayın Aralığı: Yılda 6 Sayı
Yayıncı: TÜBİTAK

Arşiv

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