The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Widar: Decimeter-Level Passive Tracking via Velocity Monitoring with Commodity Wi-Fi
Abstract
Various pioneering approaches have been proposed for Wi-Fi-based sensing, which usually employ learning-based techniques to seek appropriate statistical features, yet do not support precise tracking without prior training. Thus to advance passive sensing, the ability to track fine-grained human mobility information acts as a key enabler. In this paper, we propose Widar, a Wi-Fi-based tracking system that simultaneously estimates a human's moving velocity (both speed and direction) and location at a decimeter level. Instead of applying statistical learning techniques, Widar builds a theoretical model that geometrically quantifies the relationships between CSI dynamics and the user's location and velocity. On this basis, we propose novel techniques to identify frequency components related to human motion from noisy CSI readings and then derive a user's location in addition to velocity. We implement Widar on commercial Wi-Fi devices and validate its performance in real environments. Our results show that Widar achieves decimeter-level accuracy, with a median location error of 25 cm given initial positions and 38 cm without them and a median relative velocity error of 13%.
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Cites
From RSSI to CSI: Indoor localization via channel response
Yang, Z., Zhou, Z., & Liu, Y. (2013) · ACM Computing Surveys · doi:10.1145/2543581.2543592 · Cited by 1,229
Self-citationSpotFi: Decimeter Level Localization Using WiFi
Kotaru, M., Joshi, K., Bharadia, D., & Katti, S. (2015) · Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication · doi:10.1145/2785956.2787487 · Cited by 1,169
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
Whole-home gesture recognition using wireless signals
Pu, Q., Gupta, S., Gollakota, S., & Patel, S. (2013) · Proceedings of the 19th annual international conference on Mobile computing & networking - MobiCom '13 · doi:10.1145/2500423.2500436 · Cited by 1,109
ArrayTrack: a fine-grained indoor location system
Xiong, J., & Jamieson, K. (2013) · Cited by 821
Self-citationE-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures
Wang, Y., Liu, J., Chen, Y., Gruteser, M., Yang, J., & Liu, H. (2014) · Proceedings of the 20th annual international conference on Mobile computing and networking · doi:10.1145/2639108.2639143 · Cited by 817
Locating in fingerprint space: wireless indoor localization with little human intervention
Yang, Z., Wu, C., & Liu, Y. (2012) · Proceedings of the 18th annual international conference on Mobile computing and networking · doi:10.1145/2348543.2348578 · Cited by 739
Self-citationAdib, F., & Katabi, D. (2013) · Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM · doi:10.1145/2486001.2486039 · Cited by 698
Predictable 802.11 packet delivery from wireless channel measurements
Halperin, D., Hu, W., Sheth, A., & Wetherall, D. (2010) · Proceedings of the ACM SIGCOMM 2010 conference · doi:10.1145/1851182.1851203 · Cited by 595
3D tracking via body radio reflections
Adib, F., Kabelac, Z., Katabi, D., & Miller, R. C. (2014) · DSpace@MIT (Massachusetts Institute of Technology) · doi:10.5555/2616448.2616478 · Cited by 509
Cited by
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
Self-citationA Survey on Human Behavior Recognition Using Channel State Information
Wang, Z., Jiang, K., Hou, Y., Dou, W., Zhang, C., Huang, Z., & Guo, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2949123 · Cited by 149
XGest: Enabling Cross-Label Gesture Recognition with RF Signals
Zhang, Y., Yang, Z., Zhang, G., Wu, C., & Zhang, L. (2021) · ACM Transactions on Sensor Networks · doi:10.1145/3458750 · Cited by 29
Self-citation
Metadata
- Authors
- Qian, K., Wu, C., Yang, Z., Liu, Y., & Jamieson, K.
- Venue
- Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing
- Publisher
- ACM
- Published
- 2017-07-10
- Pages
- 1-10
- OpenAlex
- W2736051620
- Cited by
- 328
- References
- 25
- FWCI
- 40.86
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Qian, K., Wu, C., Yang, Z., Liu, Y., & Jamieson, K. (2017). Widar: Decimeter-Level Passive Tracking via Velocity Monitoring with Commodity Wi-Fi. Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing. https://doi.org/10.1145/3084041.3084067
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