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Intelligent reflecting surfaces for radar and communications

The University of Maryland, College Park

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In recent years, metasurfaces (MTSs) have shown promising abilities to control and manipulate electromagnetic (EM) waves through modified surface boundary conditions. These surfaces are electrically thin and comprise an array of spatially varying sub-wavelength scattering elements (or meta-atoms). Through careful engineering of each meta-atom, MTSs can transform an incident EM wave into an arbitrarily tailored transmitted or reflected wavefront. Recent developments in MTSs have opened exciting new opportunities in antenna design, as well as communications and radar systems. Reconfigurable MTSs - wherein meta-atoms are embedded with active components - lead to the development of low-cost, lightweight, and compact systems that can produce programmable radiation patterns, jointly perform multi-function communications, and enable advanced radars for next-generation military platforms. This talk will introduce reconfigurable MTSs and their various applications in designing simplified communications and radar systems, wherein the RF aperture and transceiver are integrated within the MTS. For example, dynamic reconfiguration of the MTS aperture in a wireless communications transmitter facilitates beam steering, frequency agility, and phase modulation without conventional front-end devices such as phase-shifters, mixers, and switches. In a synthetic aperture radar (SAR), MTSs have potential to achieve directive beams for traditional stripmap and spotlight SAR imaging modes using a low-cost compact aperture without mechanical gimbles or conventional phase-shifters. Additionally, MTSs can generate diverse radiation patterns for innovative holographic computational imaging modes, such as diverse pattern stripmap SAR. Space-time coding of MTSs has potential to realize frequency translation to achieve Doppler spoofing of reflected radar signals. Finally, we will present our recent work on reconfigurable MTS control, MTS-enabled direct signal modulation, and deep learning-based MTS design.