The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
From RSSI to CSI: Indoor localization via channel response
Open accessAbstract
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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Cites
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Cited by
Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons
He, S., & Chan, S.-H. G. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2464084 · Cited by 1,355
Understanding and Modeling of WiFi Signal Based Human Activity Recognition
Wang, W., Liu, A. X., Shahzad, M., Ling, K., & Lu, S. (2015) · Proceedings of the 21st Annual International Conference on Mobile Computing and Networking · doi:10.1145/2789168.2790093 · Cited by 1,110
Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges
Gravina, R., Alinia, P., Ghasemzadeh, H., & Fortino, G. (2017) · Information Fusion · doi:10.1016/j.inffus.2016.09.005 · Cited by 899
WiFall: Device-Free Fall Detection by Wireless Networks
Wang, Y., Wu, K., & Ni, L. M. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2016.2557792 · Cited by 836
Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi
Zheng, Y., Zhang, Y., Qian, K., Zhang, G., Liu, Y., Wu, C., & Yang, Z. (2019) · Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3307334.3326081 · Cited by 557
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Metadata
- Authors
- Yang, Z., Zhou, Z., & Liu, Y.
- Venue
- ACM Computing Surveys
- Published
- 2013-11
- Volume
- 46
- Issue
- 2
- Pages
- 1-32
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2143228105
- Cited by
- 1,229
- References
- 2
- FWCI
- 23.17
- Type
- journal article
- Language
- en
- Open access
- green
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