SuperRF: Enhanced 3D RF Representation Using Stationary Low-Cost mmWave Radar.

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Abstract

This paper introduces SuperRF- which takes radio frequency (RF) signals from an off-the-shelf, low-cost, 77GHz mmWave radar and produces an enhanced 3D RF representation of a scene. SuperRF is useful in scenarios where camera and other types of sensors do not work, or not allowed due to privacy concerns, or their performance is impacted due to bad lighting conditions and occlusions, or an alternate RF sensing system like synthetic aperture radar (SAR) is too large, inconvenient, and costly. Applications of SuperRF includes navigation and planning of autonomous and semi-autonomous systems, human-robot interactions and social robotics, and elderly and/or patient monitoring in-home healthcare scenarios. We use low-cost, off-the-shelf parts to capture RF signals and to train SuperRF. The novelty of SuperRF lies in its use of deep learning algorithm, followed by a compressed sensing-based iterative algorithm that further enhances the output, to generate a fine-grained 3D representation of an RF scene from its sparse RF representation – which a mmWave radar of the same class cannot achieve without instrumenting the system with large sized multiple antennas or physically moving the antenna over a longer period in time. We demonstrate the feasibility and effectiveness through an in-depth evaluation.