Abstract
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 (x_0, y_0) and the radius (r_p) 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
Radar cross-section
Cylinder
Scattering
Dielectric
Artificial neural network
Inverse scattering problem
Cross section (physics)
RADIUS
Physics
Optics
Radar
Codes for electromagnetic scattering by cylinders
Method of moments (probability theory)
Inverse
Moment (physics)
Mathematical analysis
Computational physics
Mathematics
Geometry
Scattering length
Computer science
Classical mechanics
Telecommunications
Artificial intelligence
Optoelectronics
Subject Areas
Microwave Imaging and Scattering Analysis
·Biomedical Engineering ·Physical Sciences
Geophysical Methods and Applications
·Ocean Engineering ·Physical Sciences
Electromagnetic Scattering and Analysis
·Atomic and Molecular Physics, and Optics ·Physical Sciences
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