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This paper proposes SquiggleMilli, a system that approximates traditional Synthetic Aperture Radar (SAR) imaging on mobile millimeter-wave (mmWave) devices. The system is capable of imaging through obstructions, such as clothing, and under low visibility conditions. Unlike traditional SAR that relies on mechanical controllers or rigid bodies, SquiggleMilli is based on the hand-held, fluidic motion of the mmWave device. It enables mmWave imaging in hand-held settings by re-thinking existing motion compensation, compressed sensing, and voxel segmentation. Since mmWave imaging suffers from poor resolution due to specularity and weak reflectivity, the reconstructed shapes could be imperceptible by machines and humans. To this end, SquiggleMilli designs a machine learning model to recover the high spatial frequencies in the object to reconstruct an accurate 2D shape and predict its 3D features and category. We have customized SquiggleMilli for security applications, but the model is adaptable to other applications with limited training samples. We implement SquiggleMilli on off-the-shelf components and demonstrate its performance improvement over the traditional SAR qualitatively and quantitatively. 

The ubiquity of millimeter-wave (mmWave) technology in 5G-and-beyond devices enable opportunities to bring through-obstruction imaging in hand-held, ad-hoc settings. This imaging technique will require manually scanning the scene to emulate a Synthetic Aperture Radar (SAR) [4] and measure back-scattered signals. Appropriate signal focusing can reveal hidden items and can be used to detect and classify shapes automatically. Such hidden object detection and classification could enable multiple applications, such as in-situ security check without pat-down search, baggage discrimination without opening the baggage, packaged inventory item counting without intrusions, etc.