Experimental evaluation of different algorithms for permittivity estimation of aeronautic materials based on metal-backed free-space measurements

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.