From RSSI to CSI: Indoor localization via channel response
Zheng Yang,Zimu Zhou,Yunhao Liu +2 more
TL;DR: In this article, the authors survey the channel state information (CSI) in 802.11 a/g/n and highlight the differences between CSI and RSSI with respect to network layering, time resolution, frequency resolution, stability, and accessibility.
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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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Passive Indoor Localization Based on CSI and Naive Bayes Classification
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Toward Location-Enabled IoT (LE-IoT): IoT Positioning Techniques, Error Sources, and Error Mitigation
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Widar3.0: Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi.
TL;DR: Widar3.0 as mentioned in this paper is a zero-effort cross-domain gesture recognition system, which is based on a one-fits-all general model that requires only one time training but can adapt to different data domains.
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Fundamental limits of RSS fingerprinting based indoor localization
Yutian Wen,Xiaohua Tian,Xinbing Wang,Songwu Lu +3 more
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TL;DR: This paper presents the probability that a user can be localized in a region with certain size, given the RSS fingerprints submitted to the system, and reveals the interaction among the localization accuracy, the reliability of location estimation and the number of measurements in the RSS fingerprinting based location determination.
Device-Free Wireless Sensing for Human Detection: The Deep Learning Perspective
TL;DR: In this article, the authors provide a comprehensive survey of the state-of-the-art research on wireless sensing for human detection with a focus on WSSs, including data acquisition for DL model training, calibration of signals from commercial devices, multimodal sensing, simultaneous user identification and activity recognition, multiuser human detection, and generalization ability of DL models.
127
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