Open Access
From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information
Yang Zheng,Zhou Zimu,Yunhao Liu +2 more
- 01 Jan 2014
681
TL;DR: This article surveys the new trend of channel response in localization and investigates a large body of recent works and classify them overall into three categories according to how to use CSI, highlighting the differences between CSI and RSSI.
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Abstract: The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.
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Citations
A Survey of Indoor Localization Systems and Technologies
TL;DR: This paper aims to provide a detailed survey of different indoor localization techniques, such as angle of arrival (AoA), time of flight (ToF), return time ofFlight (RTOF), and received signal strength (RSS) based on technologies that have been proposed in the literature.
2.2K
Understanding and Modeling of WiFi Signal Based Human Activity Recognition
Wei Wang,Alex X. Liu,Muhammad Shahzad,Kang Ling,Sanglu Lu +4 more
- 07 Sep 2015
TL;DR: CARM is a CSI based human Activity Recognition and Monitoring system that quantitatively builds the correlation between CSI value dynamics and a specific human activity and recognizes a given activity by matching it to the best-fit profile.
WiFi Sensing with Channel State Information: A Survey
TL;DR: This survey gives a comprehensive review of the signal processing techniques, algorithms, applications, and performance results of WiFi sensing with CSI, and presents three future WiFi sensing trends, i.e., integrating cross-layer network information, multi-device cooperation, and fusion of different sensors for enhancing existing WiFi sensing capabilities and enabling new WiFi sensing opportunities.
688
Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi
Yue Zheng,Yi Zhang,Kun Qian,Guidong Zhang,Yunhao Liu,Chenshu Wu,Zheng Yang +6 more
- 12 Jun 2019
TL;DR: Widar3.0 is the first zero-effort cross-domain gesture recognition work via Wi-Fi, a fundamental step towards ubiquitous sensing and a one-fits-all model that requires only one-time training but can adapt to different data domains.
524
Device-Free Human Activity Recognition Using Commercial WiFi Devices
TL;DR: A Channel State Information (CSI)-based human Activity Recognition and Monitoring system (CARM) based on a CSI-speed model that quantifies the relation between CSI dynamics and human movement speeds and human activities.
487
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Predictable 802.11 packet delivery from wireless channel measurements
Daniel Halperin,Wenjun Hu,Anmol Sheth,David Wetherall +3 more
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TL;DR: It is shown that, for the first time, wireless packet delivery can be accurately predicted for commodity 802.11 NICs from only the channel measurements that they provide, and the rate prediction is as good as the best rate adaptation algorithms for 802.
•Proceedings Article
ArrayTrack: a fine-grained indoor location system
Jie Xiong,Kyle Jamieson +1 more
- 02 Apr 2013
TL;DR: In this paper, the authors present an indoor location system that uses MIMO-based techniques to track wireless clients at a very fine granularity in real time, as they roam about a building.
WiFi Sensing with Channel State Information: A Survey
TL;DR: This survey gives a comprehensive review of the signal processing techniques, algorithms, applications, and performance results of WiFi sensing with CSI, and presents three future WiFi sensing trends, i.e., integrating cross-layer network information, multi-device cooperation, and fusion of different sensors for enhancing existing WiFi sensing capabilities and enabling new WiFi sensing opportunities.
688