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Rui-Heng Tu

dblp:323/2590 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Wireless sensing and localization · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Smart cities and intelligent transportation · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless sensing and localization › localization algorithms
GPS-denied localization
0.512021
MVP: magnetic vehicular positioning system for GNSS-denied environments · MobiCom 2021
Wireless sensing and localization › indoor localization › fingerprint-based localization
magnetic fingerprinting
0.512021
MVP: magnetic vehicular positioning system for GNSS-denied environments · MobiCom 2021
Wireless sensing and localization
vehicle localization
0.512021
MVP: magnetic vehicular positioning system for GNSS-denied environments · MobiCom 2021
Smart cities and intelligent transportation › navigation
vehicle navigation
0.112021
MVP: magnetic vehicular positioning system for GNSS-denied environments · MobiCom 2021

Methods — techniques the papers use, named apart from their topics

magnetic fingerprint matching · 1.0
YearPublicationVenuePosition
2021 MVP: magnetic vehicular positioning system for GNSS-denied environments
abstract
Accurate positioning in global navigation satellite system (GNSS)-denied environments, such as tunnels and underpasses, remains a challenge. Navigation systems for such environments need to strike a balance between price and precision. In this paper, we propose magnetic vehicular positioning (MVP), a navigation system that guides drivers in GNSS-denied environments. The key idea of MVP is to extract magnetic fingerprints from geomagnetic field anomalies. By comparing the measured magnetic field against a magnetic map, positioning can be achieved without GNSS signals. Our proposed matching algorithm allows MVP to provide 5.14 m positioning accuracy. We conducted large-scale real-road experiments for 36 months in two countries and 56 tunnels to demonstrate the effectiveness of the proposed system. Because MVP can be deployed on off-the-shelf smartphones, our approach makes accurate navigation in GNSS-denied environments affordable.
Chia-Cheng Wang, Jyh-Cheng Chen, Rui-Heng Tu, Jia-Jiun Lee, Yu-Xin Xiao, Shan-Yu Cai
MobiCom4