Deep Learning-Enabled Invisible Electromagnetic Scattering Amplifier
Qike Xie, Qin Liao, Xiaofan Ji, Yichao Liu, Fei Sun

TL;DR
This paper introduces an electromagnetic device that simultaneously remains invisible and amplifies the scattering signals of micro-targets, enhancing detection capabilities without revealing its own presence.
Contribution
It presents a novel design combining electromagnetic invisibility with scattering amplification using neural networks, overcoming previous limitations of separate functionalities.
Findings
Achieves up to 8.58 times scattering amplification for subwavelength targets.
Maintains invisibility while amplifying scattering regardless of target position or shape.
Demonstrates potential applications in radar detection and security.
Abstract
With the rapid development of micro-electro-mechanical systems, electrically small micro-targets, such as subwavelength micro unmanned aerial vehicles and bionic mosquito robots, exhibit ultra-low scattering cross section, which brings severe challenges to their effective detection. To address this problem, an Invisible Electromagnetic Scattering Amplifier (IESA) is designed by combining finite-element electromagnetic simulation with a forward lossless tandem neural network. The IESA realizes the dual-functional integration of intrinsic electromagnetic invisibility (near-zero scattering) for itself and significant scattering amplification for subwavelength targets entering its air sensing region. Electromagnetic simulations verify that the designed IESA can achieve a stable scattering amplification effect on subwavelength targets with a characteristic size of approximately…
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Taxonomy
TopicsAdvanced Wireless Communication Technologies · Metamaterials and Metasurfaces Applications · Energy Harvesting in Wireless Networks
