Microwave detection and dielectric characterization of cylindrical objects from amplitude-only data by means of neural networks

In this paper, we propose a new microwave technique for the localization and the dielectric characterization of physically inaccessible cylindrical objects from amplitude-only data. By means of a neural network used to solve the inverse scattering problem; this technique allows to directly achieve the object retrieval, avoiding the drawbacks related to the measurement of the phase distribution of the field that generally represent a critical point, especially at high frequency. The efficiency of the proposed technique in the reconstruction of both the position and the dielectric properties of a circular cylindrical body from amplitude-only information is illustrated and compared with the reconstruction performances of. a neural network imaging technique that makes use of both amplitude and phase of the scattered field. The presence of noisy data is also taken into account, showing the dependence of the reconstruction accuracy on the signal-to-noise-ratio.