Prof. Axel Bangert Profile

Prof. Axel Bangert

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Proceedings Article | 30 November 2023 Paper

Machine-learning-based microwave ghost imaging using reconfigurable metasurface

Yash Fafolawala, Axel Bangert, Samaneh Moeini

Proceedings Volume 12796, 1279602 (2023) https://doi.org/10.1117/12.2690801

KEYWORDS: Microwave radiation, Image restoration, Target detection, Waveguides, Simulations, Reflection, PIN photodiodes, Neural networks, Education and training, Diodes

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This paper presents a machine-learning based approach to microwave ghost imaging. Unlike traditional imaging techniques, ghost imaging is a non-local imaging technique that does not involve scanning. Since ghost imaging uses a single detector at a fixed location, measurements are taken under various random or pseudo-random illuminations to recover the image. Moving from the optical frequency regime to the microwave regime, the accurate measurement of such a random field becomes more complicated because there is no device such as a Charge Coupled Device (CCD) camera at microwaves. In this work, we generate pseudo-random field patterns using a reconfigurable metasurface and we use a single detector at a fixed location to measure the reflection from the target. Reconfigurability plays a fundamental role in ghost imaging. The microstrip line-fed metasurface is formed by unit cells of size 2.564 × 1 mm², which are tuned by a pair of PIN diodes. By activating these diodes in forward or reverse bias, different field patterns can be generated. A receiver is placed in the centre of the aperture. The resonance frequency is set to 40 GHz. To recover the image, we present a machine-learning approach. A neural network is trained using a synthetic dataset obtained from ANSYS High Frequency Structure Simulator (HFSS) simulations to learn the second-order correlation between the incident field patterns and the measured scattered fields at the detector in order to recover the target image.

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