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Radar Micro‐Doppler Signature Generation Based on Time‐Domain Digital Coding Metasurface
Radar Micro‐Doppler Signature Generation Based on Time‐Domain Digital Coding Metasurface
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Radar Micro‐Doppler Signature Generation Based on Time‐Domain Digital Coding Metasurface
Radar Micro‐Doppler Signature Generation Based on Time‐Domain Digital Coding Metasurface

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Radar Micro‐Doppler Signature Generation Based on Time‐Domain Digital Coding Metasurface
Radar Micro‐Doppler Signature Generation Based on Time‐Domain Digital Coding Metasurface
Journal Article

Radar Micro‐Doppler Signature Generation Based on Time‐Domain Digital Coding Metasurface

2024
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Overview
Micro‐Doppler effect is a vital feature of a target that reflects its oscillatory motions apart from bulk motion and provides an important evidence for target recognition with radars. However, establishing the micro‐Doppler database poses a great challenge, since plenty of experiments are required to get the micro‐Doppler signatures of different targets for the purpose of analyses and interpretations with radars, which are dramatically limited by high cost and time‐consuming. Aiming to overcome these limits, a low‐cost and powerful simulation platform of the micro‐Doppler effects is proposed based on time‐domain digital coding metasurface (TDCM). Owing to the outstanding capabilities of TDCM in generating and manipulating nonlinear harmonics during wave‐matter interactions, it enables to supply rich and high‐precision electromagnetic signals with multiple micro‐Doppler frequencies to describe the micro‐motions of different objects, which are especially favored for the training of artificial intelligence algorithms in automatic target recognition and benefit a host of applications like imaging and biosensing. A low‐cost and high‐flexible radar micro‐Doppler signature generation platform is proposed based on metasurface. The presented metasurface contains time‐varying modulation periods, thus capable of supplying the electromagnetic signals with designable micro‐Doppler frequencies to describe micro‐motions of different objects. The proposed method is especially favored for the training of AI algorithms and benefits a host of applications like imaging and biosensing.