SuperRF: Enhanced 3D RF Representation Using Stationary Low-Cost mmWave Radar. Article Swipe
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.
Related Topics
Concepts
Computer science
Radio frequency
Radar
Artificial intelligence
Synthetic aperture radar
Real-time computing
Representation (politics)
Antenna (radio)
Computer vision
Radar imaging
Robotics
Robot
Electronic engineering
Remote sensing
Telecommunications
Engineering
Law
Politics
Geology
Political science
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://pubmed.ncbi.nlm.nih.gov/32704622
- OA Status
- green
- Cited By
- 8
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3029178032
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3029178032Canonical identifier for this work in OpenAlex
- Title
-
SuperRF: Enhanced 3D RF Representation Using Stationary Low-Cost mmWave Radar.Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-02-01Full publication date if available
- Authors
-
Shiwei Fang, Shahriar NirjonList of authors in order
- Landing page
-
https://pubmed.ncbi.nlm.nih.gov/32704622Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/7377925Direct OA link when available
- Concepts
-
Computer science, Radio frequency, Radar, Artificial intelligence, Synthetic aperture radar, Real-time computing, Representation (politics), Antenna (radio), Computer vision, Radar imaging, Robotics, Robot, Electronic engineering, Remote sensing, Telecommunications, Engineering, Law, Politics, Geology, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 2, 2022: 4, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.to | 46, 55, 108, 113, 140 |
| abstract_inverted_index.The | 116 |
| abstract_inverted_index.and | 17, 34, 59, 76, 83, 87, 92, 95, 112, 190 |
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| abstract_inverted_index.due | 45, 54 |
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| abstract_inverted_index.camera | 33 |
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| abstract_inverted_index.mmWave | 15, 158 |
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| abstract_inverted_index.system | 66, 169 |
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| abstract_inverted_index.novelty | 117 |
| abstract_inverted_index.output, | 139 |
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| abstract_inverted_index.privacy | 47 |
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| abstract_inverted_index.sensors | 38 |
| abstract_inverted_index.signals | 9, 111 |
| abstract_inverted_index.through | 192 |
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| abstract_inverted_index.SuperRF- | 3 |
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| abstract_inverted_index.antennas | 174 |
| abstract_inverted_index.aperture | 69 |
| abstract_inverted_index.enhanced | 20 |
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| abstract_inverted_index.followed | 128 |
| abstract_inverted_index.generate | 141 |
| abstract_inverted_index.impacted | 53 |
| abstract_inverted_index.in-depth | 194 |
| abstract_inverted_index.includes | 81 |
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| abstract_inverted_index.lighting | 57 |
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| abstract_inverted_index.alternate | 63 |
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| abstract_inverted_index.frequency | 7 |
| abstract_inverted_index.iterative | 133 |
| abstract_inverted_index.low-cost, | 13, 105 |
| abstract_inverted_index.robotics, | 94 |
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| abstract_inverted_index.autonomous | 86 |
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| abstract_inverted_index.human-robot | 90 |
| abstract_inverted_index.occlusions, | 60 |
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| abstract_inverted_index.Applications | 78 |
| abstract_inverted_index.fine-grained | 143 |
| abstract_inverted_index.interactions | 91 |
| abstract_inverted_index.effectiveness | 191 |
| abstract_inverted_index.inconvenient, | 75 |
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| abstract_inverted_index.off-the-shelf | 106 |
| abstract_inverted_index.sensing-based | 132 |
| abstract_inverted_index.off-the-shelf, | 12 |
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| abstract_inverted_index.semi-autonomous | 88 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 93 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.6600000262260437 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.72834864 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |