Precise indoor localization using PHY layer information

ABSTRACT

This paper shows the viability of precise indoor localization using physical layer information in WiFi systems. We find that channel frequency responses across multiple OFDM sub-carriers can be suitably aggregated into a location fingerprint. While these fingerprints vary over time and environmental mobility, we notice that their core structure preserves certain properties that are amenable to localization. We demonstrate these ideas through a functional prototype, implemented on off-the-shelf Intel 5300 cards (that export per-subcarrier information to the driver). We evaluate the prototype using the existing APs inside a busy building, a cafeteria, and a museum, and demonstrate localization accuracies in the granularity of 1m × 1m boxes, called spots. Results show that our system, PinLoc, is able to localize users to a spot with 90% mean accuracy, while incurring less than 6% false positives. We believe this holds promise towards an important development in indoor localization.

References

1. Placecast. Shopalerts. http://placecast.net/shopalerts/index.html.
2. E. Bruns et al. Enabling mobile phones to support large-scale museum guidance. Multimedia, IEEE, 2007.
3. N. B. Priyantha. The cricket indoor location system. PhD thesis.
4. V. Bahl et al. RADAR: An in-building rf-based user location and tracking system. In INFOCOM, 2000.
5. Yu-Chung et al. Accuracy characterization for metropolitan-scale wi-fi localization. In MobiSys, 2005.
6. M. Youssef and A. Agrawala. The horus WLAN location determination system. In MobiSys, 2005.
7. K. Chintalapudi, A. Iyer, and V. Padmanabhan. Indoor localization without the pain. In MOBICOM, 2010.
8. Junxing Zhang et al. Advancing wireless link signatures for location distinction. In MobiCom. ACM, 2008.
9. Intel Research. Intel 5300 mimo channel measurement tool. http://ils.intel-research.net/80211n-channel-measurement-tool.
10. D. Tse and P. Viswanath. Fundamentals of Wireless Communication. Cambridge University Press, 2005.
11. C. Bishop. Pattern Recognition and Machine Learning. Springer.
12. T. Minka, J. M. Winn, J. P. Guiver, and D. A. Knowles. Infer.NET 2.4, 2010. Microsoft Research Cambridge.
13. William G. Griswold et al. Activecampus: Experiments in community-oriented ubiquitous computing. Computer, 2004.
14. M. Youssef et al. Pinpoint: An asynchronous time-based location determination system. In ACM Mobisys, June 2006.
15. X. Cheng et al. TPS: A time-based positioning scheme for outdoor sensor networks. In IEEE Infocom, March 2004.
16. A. Smith, Balakrishnan, et al. Tracking moving devices with the cricket location system. In ACM Mobisys, June 2004.
17. J. Xiong and K. Jamieson. SecureAngle: improving wireless security using angle-of-arrival information. In ACM HotNets, 2010.

Index Terms

• Networks

  • Network types

    • Mobile networks

    • Wireless access networks