Electromagnetic detection of dielectric cylinders by a neural network approach

In this paper, the neural network approach is applied to the detection of cylindric objects as well as their geometric and electrical characteristics inside a given investigation domain. The electric field values scattered by the object and available at a small number of locations are fed into the network, whose output is the dielectric permittivity, and the location and radius of the cylinder. The results are evaluated using different sets of testing data, and the dependence of the various output parameters to the input are considered. The algorithm performance shows that the approach is able to solve the inverse scattering problem quickly. This may be useful for real-time remote-sensing applications.