A neural network-informed self-aware deployable structure with application to phased array antennas

Abstract

Abstract State of the art radio frequency (RF) arrays are growing larger in pursuit of increased signal-to-noise ratio. In support of this goal, elaborate forms of metrology are being developed to support the increased footprints. This work provides a unique solution to fulfill the metrology requirements of large-scale deployable RF antennas through the implementation of neural network demodulation of fiber optic strain sensors. The fiber optics are patterned with fiber Bragg gratings (FBGs) to encode strain on to back-reflected shifts in the wavelength of incident light. Experiments show the neural network can predict the deformation of a test structure within single millimeters for small amplitude motions. Therefore, the current technique meets the required λ / 20 precision needed for large scale deployable RF arrays operating at S-band or longer wavelengths.