SquiggleMilli

Abstract

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.

A Survey of mmWave-Based Human Sensing: Technology, Platforms and Applications

“…Different from the existing vision, IR, and X-ray imaging systems, mmWave signals can penetrate clothes and work in low visibility conditions. Due to its higher privacy and millimeter-scale ranging resolution, mmWave-based imaging has been widely pursued for pose/posture tracking [30] [112], automatic driving [114], security concerns [65] [113] [119], etc. We summarize the related works in TABLE VIII.…”

“…Given three 2D facial photos taken from different perspectives, mmFace can reconstruct a virtual scene and generate the reflected mmWave signals by building a Unfortunately, the pre-determined trajectory and uniform sampling make it extremely challenging to emulate SAR on a hand-held device. To this end, SquiggleMilli [112] uses a non-linear motion compensation and a compressed sensingbased framework to generate uniformly and densely spaced grid locations, as the traditional SAR imaging system does. As a user freely moves the device in the air, SquiggleMilli maps the measured sample to the nearest point on the uniform grid according to its phase change.…”

DI-Gesture: Domain-Independent and Real-Time Gesture Recognition with Millimeter-Wave Signals

“…Driven by the need for compact and portable imaging systems, researchers have explored replacing mechanical scanning with handheld scanning to achieve mmWave SAR imaging [23][24][25][26][27]. One promising application of such handheld systems is security checks at train stations or airports.…”

Short-Time Coherent–Incoherent Synthetic Aperture Radar Processing for a Handheld Imaging System

“…One of the challenges faced by this kind of systems is that the measurements are not acquired uniformly, which prevents the straightforward use of highly efficient algorithms based on fast Fourier Transforms [22]. This is a constraint also inherent to other vehicle or UAV mounted [5] systems, and a significant research effort has been devoted to develop efficient algorithms to process this kind of data [23], [24], [25], [26]. Moreover, the inspected area might be sparsely sampled, specially at the beginning of the scanning process, causing undesired artifacts in the reflectivity images of the scene.…”