MasterThesis

Machine-Learning Enhanced Microwave Ghost Imaging Using Electronically Reconfigurable Metasurface

Abstract

For imaging applications, conventional techniques usually require amplitude and phase data of the signals being captured. Ghost imaging (GI) is a novel imaging technique rooted in quantum optics, which is non-local and does not require phase measurements of the received signals. In the optical regime, a fixed single-pixel detector, which captures modulated signals, and a scanning probe detector which captures reference signals, are used. Using computational methods, it is possible to reconstruct an image using only a single-pixel detector, having known the random or pseudo-random modulation patterns. Adopting this technique in the microwave regime requires a device which can generate pseudo-random illumination patterns. In this thesis, a metasurface is utilised, a finite array of unitcells with resonator element of dimension 1.25 x 0.7 mm2. Its geometrical shape, size and features are designed in a way that it resonates at 40 GHz. The field patterns emitted from this device can be altered by employing two positive-intrinsic-negative (PIN) diodes which make the metasurface electronically reconfigurable. By controlling these diodes individually for twenty such unitcells can generate pseudo-random radiation patterns which are recorded in the far field where the object is lying. A fixed point-like receiver located in the centre of the metasurface aperture captures the fields scattered from the object. So the transmitting aperture can be reprogrammed in real-time. A synthetic dataset is built using the simulations and analytical computations for 1500 pseudo-random incident fields. To reduce the manual computational burden, a machine-learning-based artificial neural network (ANN) is employed to solve the inverse imaging problem in this feasibility study. It feeds on the features from the synthetic dataset, trains on the sampled dataset and tests on the remaining dataset. This approach enables its application in surveillance systems, non-destructive imaging and non-line-of-sight imaging by leveraging the fast computation of machine-learning (ML).