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Experimental evaluation of different algorithms for permittivity estimation of aeronautic materials based on metal-backed free-space measurements
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1  Detectability and Electronic Warfare Laboratory, National Institute for Aerospace Technology (INTA), Torrejón de Ardoz, 28850 Madrid, Spain


Current small unmanned aerial vehicles (UAV) or target drones used for artillery training are being made of materials other than metallic, such as carbon-fiber or fiberglass composites. From the electromagnetic point of view this fact forces engineers and scientists to assess how these changes may affect the structure in terms of, for instance, electromagnetic compatibility (EMC) or radar response. In order to do so, estimations of the constitutive parameters of these new materials has become a need. Several techniques exist to perform this kind of estimations, all of them based on the utilization of different sensors. For this paper, an own implementation of the so-called metal-backed free-space technique, based on the employment of antenna probes, is utilized. Apart from the technique itself, different extraction algorithms can be chosen.

In this regard, this paper examines the behavior of several algorithms when applied to extract the complex permittivity from the reflection coefficient of a set of materials. The algorithms studied include Pattern Search, Genetic Algorithms, Particle Swarm Optimization, Newton Search and Müller Search. First, the algorithms are analyzed using simulated reflection coefficient data. Then, those that perform better are applied to actual measured reflection coefficients of fiberglass materials that form part of a target drone employed by INTA. The results are compared with the estimations given by a commercial solution based on another sensor and technique: the open ended coaxial probe. Proper conclusions are extracted regarding the performance of the adopted approach.

Keywords: antenna probes; permittivity; materials characterization; free-space; fiberglass
Comments on this paper
Guillermo Robles
Particle swarm optimization
Nice paper,

We have some experience with PSO, in fact, we use it in our paper in this conference to minimize an objetive function to locate radio-frequency sources (Antenna array layout for the localization of partial discharges in open-air substations -

I have read in your paper that you have convergence problems especially at high frequencies. I would like to know whether you have changed the initial deployment of particles in different tests. In some ocasions, it can help to have faster and more accurate convergences. Additionally, have you tested changes in the inertia and/or the random coefficients?

David Escot Bocanegra
Hi Guillermo,

Thanks for your kind interest. This was a very first approach and we used PSO in this paper for the sake of comparing a bio-inspired algorithm with other traditional algorithms but, as you accurately point out, we didn't varied the parameters of PSO in our experiments. A deeper analysis of the results is under study and if we found that PSO can be more useful than traditional methods for this particular application we will, for sure, conduct a parametric analysis.

Guillermo Robles
Thanks for the reply!