A Stochastic Framework for the Variability Analysis of Textile Antennas

Abstract

A novel framework to accurately quantify the effect of stochastic variations of design parameters on the performance of textile antennas is developed and tested. First, a sensitivity analysis is applied to get a rough idea about the effect of these random variations on the textile antenna's performance. Next, a more detailed view is obtained by a generalized polynomial chaos technique that accurately quantifies the statistical distribution of the textile antenna's figures of merit, for a given range over which geometry and material parameters vary statistically according to a given distribution. The method is validated both for a simple inset-fed patch textile microstrip antenna and for a dual-polarized textile antenna. For the latter, the probability density function corresponding to its most sensitive design parameter, being the width, is experimentally estimated by means of measurements performed on 100 patches. A Kolmogorov-Smirnoff test proves that, for all considered examples, the results are as accurate as those obtained via Monte Carlo analysis, while the new technique is much more efficient. Indeed, speedups up to a factor 1667 are demonstrated.