Document Type

Conference Proceeding

Publication Title

Proceedings of SPIE - the International Society for Optical Engineering


Artificial neural networks (ANNs) and their ability to model and control dynamical systems for smart structures, including sensors, actuators, and plants, are directly applicable to the smart electromagnetic structures (SEMS) concept. The application of neural networks to the area of controls is being reported frequently. The ability of a structure to adapt to impinging electromagnetic (EM) energy will allow the structure to change its reflection characteristics and thus to change its radar signature. By embedding a control element in the structure of a single microstrip patch element, its electrical characteristics can be changed. If such an element can be controlled by a closed loop system the patch antenna element can be made to adjust its operating characteristics through the control algorithm. If the control algorithm can be implemented in a neural network, the system can be made to change its characteristics in response to the stimulus. This change can be used to alter the antenna's performance in real time. As part of our research, a model of the patch neural network antenna system is being developed and this analytical model, as well as experimental models of the antenna are being tested and compared. The neural network antenna model and prototypes are being taught to adapt to the magnitude and phase response of microstrip patch antennas to incoming signals. The response characteristics and speed are reported in this paper. We demonstrate that the patch can be given autonomous adaptive capabilities using neural networks. An array of such smart patches could be assembled to create an even more adaptable antenna system.

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