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Using millimeter wave (mmWave) signals for imaging has an important advantage in that they can penetrate through poor environmental conditions such as fog, dust, and smoke that severely degrade optical-based imaging systems. However, mmWave radars, contrary to cameras and LiDARs, suffer from low angular resolution because of small physical apertures and conventional signal processing techniques. Sparse radar imaging, on the other hand, can increase the aperture size while minimizing the power consumption and read out bandwidth. This paper presents CoIR, an analysis by synthesis method that leverages the implicit neural network bias in convolutional decoders and compressed sensing to perform high accuracy sparse radar imaging. The proposed system is data set-agnostic and does not require any auxiliary sensors for training or testing. We introduce a sparse array design that allows for a 5.5× reduction in the number of antenna elements needed compared to conventional MIMO array designs. We demonstrate our system's improved imaging performance over standard mmWave radars and other competitive untrained methods on both simulated and experimental mmWave radar data.
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R-fiducial: Millimeter Wave Radar Fiducials for Sensing Traffic Infrastructure
Millimeter wave (mmWave) sensing has recently gained attention for its robustness in challenging environments. When visual sensors such as cameras fail to perform, mmWave radars can be used to provide reliable performance. However, the poor scattering performance and lack of texture in millimeter waves can make it difficult for radars to identify objects in some situations precisely. In this paper, we take insight from camera fiducials which are very easily identifiable by a camera, and present R-fiducial tags, which smartly augment the current infrastructure to enable myriad applications with mmwave radars. R-fiducial acts as fiducials for mmwave sensing, similar to camera fiducials, and can be reliably identified by a mmwave radar. We identify a set of requirements for millimeter wave fiducials and show how R-fiducial meets them all. R-fiducial uses a novel spread-spectrum modulation technique to provide low latency with high reliability. Our evaluations show that R-fiducial can be reliably detected with a 100% detection rate up to 25 meters with a 120-degree field of view and a few milliseconds of latency. We also conduct experiments and case studies in adverse and low visibility conditions to demonstrate the potential of R-fiducial in a variety of applications.
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- PAR ID:
- 10457419
- Date Published:
- Journal Name:
- 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)
- Page Range / eLocation ID:
- 1 to 7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/VTC2023-Spring57618.2023.10199374
