In this study, we intended to verify simulations and measured data to support the development of an ultra-small and low power, handheld, or drone-carried ultra-wideband impulse radar (IR). Such a radar can remotely detect layers in snow or ice that tend to crack or break under certain conditions. First, we introduce the basic hardware design and configuration as a background, then we developed a series of electromagnetics sensing models, which can support training and testing of an algorithm based on machine-learning (ML), since the time-domain radar signatures of those hazardous structures are not widely available. We compared the principles and performance of these computational models and validated them with lab measurements and some initial snow measurements.
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