Frequency selective surface-based sensing: Theory and applications

Frequency selective surfaces (FSS) are periodic arrays of resonant elements with a specific (resonant) reflection/transmission response when illuminated by electromagnetic energy. FSSs have been utilized for different applications such as spatial filters, reflectors, lenses, radomes, and more recently, as sensors. FSS-based sensors have shown potential for numerous applications in structural health monitoring such as crack detection, concurrent strain and temperature sensing, normal and shear strain sensing, inspection of layered structures, etc. As FSS-based sensing is largely undeveloped, there are many critical aspects that must be fully understood before this sensing approach can be fully utilized. Therefore, the goal of this research is to advance the science behind FSS-based sensing in order to create a platform of knowledge upon which future engineers may utilize when designing FSS-based sensors. To this end, the theoretical (assuming infinite dimensions and a uniform excitation) FSS response is modeled using a cavity-based coupled-mode theory and subsequent quality factor analysis for patch and loop unit cells in order to study the effect of unit cell dimension, element geometry and substrate properties on the FSS frequency response. In addition, the differences between theoretical and practical FSSs are studied in order to obtain design rules and metrics to achieve a reliable (localized) sensing measurement by an FSS sensor, thereby improving the sensing resolution (from the dimensions of the sensor to smaller \u27cells\u27 within the sensor). Then, to achieve the maximum resolution of the FSS sensor, an approach is presented to determine the optimal sensor cell size. Additionally, a method using synthetic beamforming is presented to obtain an adaptive resolution for FSS sensing --Abstract, page iv