Yang Zhao
Neal Patwari
ABSTRACT
Network radio frequency (RF) environment sensing (NRES) systems pinpoint and track people in buildings using changes in the signal strength measurements made by a wireless sensor network. It has been shown that such systems can locate people who do not participate in the system by wearing any radio device, even through walls, because of the changes that moving people cause to the static wireless sensor network. However, many such systems cannot locate stationary people. We present and evaluate a system which can locate stationary or moving people, without calibration, by using kernel distance to quantify the difference between two histograms of signal strength measurements. From five experiments, we show that our kernel distance-based radio tomographic localization system performs better than the state-of-the-art NRES systems in different non line-of-sight environments.
- Camero website. http://www.camero-tech.com.Google Scholar
- Sensing and Processing Across Networks (SPAN) Lab, Spin website. http://span.ece.utah.edu/spin.Google Scholar
- P. Bahl and V. N. Padmanabhan. RADAR: an in-building RF-based user location and tracking system. In IEEE INFOCOM 2000, volume 2, pages 775--784, 2000.Google ScholarCross Ref
- C. Chang and A. Sahai. Object tracking in a 2D UWB sensor network. In 38th Asilomar Conference on Signals, Systems and Computers, volume 1, pages 1252--1256, Nov. 2004.Google ScholarCross Ref
- X. Chen, A. Edelstein, Y. Li, M. Coates, M. Rabbat, and A. Men. Sequential monte carlo for simultaneous passive device-free tracking and sensor localization using received signal strength measurements. In Proc. ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Chicago, U.S., April 2011.Google Scholar
- D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(5):603--619, 2002. Google ScholarDigital Library
- T. Cover and J. A. Thomas. Elements of Information Theory. John Wiley & Sons, 1991. Google ScholarDigital Library
- G. D. Durgin. Space-Time Wireless Channels. Prentice Hall PTR, 2002. Google ScholarDigital Library
- A. Edelstein and M. Rabbat. Background subtraction for online calibration of baseline rss in rf sensing networks. Technical Report arXiv:1207.1137v1, Arxiv.org, July 2012.Google Scholar
- A. M. Haimovich, R. S. Blum, and L. J. Cimini. MIMO radar with widely separated antennas. IEEE Signal Processing, 25(1):116--129, Jan. 2008.Google ScholarCross Ref
- S. Joshi, R. V. Kommaraji, J. M. Phillips, and S. Venkatasubramanian. Comparing distributions and shapes using the kernel distance. In Proceedings of the 27th annual ACM symposium on Computational geometry, SoCG '11, pages 47--56, 2011. Google ScholarDigital Library
- O. Kaltiokallio and M. Bocca. Real-time intrusion detection and tracking in indoor environment through distributed RSSI processing. In IEEE 17th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), Toyama, Japan, August 2011. Google ScholarDigital Library
- M. A. Kanso and M. G. Rabbat. Efficient detection and localization of assets in emergency situations. In 3rd Intl. Symposium on Medical Information & Communication Technology (ISMICT), Montréal, Québec, Feb. 2009.Google Scholar
- J. M. Lucas and M. S. Saccucci. Exponentially weighted moving average control schemes: Properties and enhancements. Technometrics, 32(1):1--12, 1990. Google ScholarDigital Library
- M. Maheshwari, S. A. P.R., A. Banerjee, N. Patwari, and S. K. Kasera. Detecting malicious nodes in rss-based localization. In Proceedings of the 2nd IEEE International Workshop on Data Security and Privacy in Wireless Networks (D-SPAN), pages 1--6, June 2011. Google ScholarDigital Library
- G. Mao, B. Fidan, and B. D. O. Anderson. Wireless sensor network localization techniques. Comput. Networks, 51(10):2529--2553, 2007. Google ScholarDigital Library
- M. Moussa and M. Youssef. Smart services for smart environments: Device-free passive detection in real environments. In IEEE PerCom-09, pages 1--6, 2009. Google ScholarDigital Library
- N. Patwari and P. Agrawal. Effects of correlated shadowing: Connectivity, localization, and rf tomography. In IEEE/ACM IPSN'08, April 2008. Google ScholarDigital Library
- N. Patwari, J. Ash, S. Kyperountas, R. M. Moses, A. O. Hero III, and N. S. Correal. Locating the nodes: Cooperative localization in wireless sensor networks. IEEE Signal Process., 22(4):54--69, July 2005.Google ScholarCross Ref
- N. Patwari and J. Wilson. RF sensor networks for device-free localization: Measurements, models and algorithms. Proceedings of the IEEE, 98(11):1961--1973, Nov. 2010.Google ScholarCross Ref
- J. M. Phillips and S. Venkatasubramanian. A gentle introduction to the kernel distance. Technical Report arXiv:1103.1625, Arxiv.org, 2011.Google Scholar
- M. Seifeldin and M. Youssef. Nuzzer: A large-scale device-free passive localization system for wireless environments. Technical Report arXiv:0908.0893, Arxiv.org, Aug. 2009.Google Scholar
- J. Wilson and N. Patwari. Radio tomographic imaging with wireless networks. IEEE Transactions on Mobile Computing, 9(5):621--632, May 2010. Google ScholarDigital Library
- J. Wilson and N. Patwari. See-through walls: Motion tracking using variance-based radio tomography networks. IEEE Transactions on Mobile Computing, 10(5):612--621, May 2011. Google ScholarDigital Library
- J. Wilson and N. Patwari. A fade level skew-laplace signal strength model for device-free localization with wireless networks. IEEE Transactions on Mobile Computing, 11:947 -- 958, June 2012. Google ScholarDigital Library
- K. Woyach, D. Puccinelli, and M. Haenggi. Sensorless Sensing in Wireless Networks: Implementation and Measurements. In Second International Workshop on Wireless Network Measurement (WiNMee'06), April 2006.Google Scholar
- C. Xu, B. Firner, Y. Zhang, R. Howard, J. Li, and X. Lin. Improving RF-based device-free passive localization in cluttered indoor environments through probabilistic classification methods. In Proc. 11th Int. Conf. Information Processing in Sensor Networks, pages 209--220, 2012. Google ScholarDigital Library
- M. Youssef, M. Mah, and A. Agrawala. Challenges: device-free passive localization for wireless environments. In MobiCom '07: ACM Int'l Conf. Mobile Computing and Networking, pages 222--229, 2007. Google ScholarDigital Library
- D. Zhang, J. Ma, Q. Chen, and L. M. Ni. An RF-based system for tracking transceiver-free objects. In IEEE PerCom'07, pages 135--144, 2007. Google ScholarDigital Library
- Y. Zhao and N. Patwari. Noise reduction for variance-based device-free localization and tracking. In Proc. of the 8th IEEE Conf. on Sensor, Mesh and Ad Hoc Communications and Networks (SECON'11), June 2011.Google ScholarCross Ref
- Y. Zhao and N. Patwari. Robust estimators for variance-based device-free localization and tracking. Technical Report arXiv:1110.1569v1, Arxiv.org, Oct. 2011.Google Scholar
- Y. Zhao and N. Patwari. Histogram distance-based radio tomographic localization. In Proceedings of the 11th international conference on Information Processing in Sensor Networks, IPSN '12, pages 129--130. ACM, 2012. Google ScholarDigital Library
- Y. Zheng and A. Men. Through-wall tracking with radio tomography networks using foreground detection. In Proceedings of the Wireless Communications and Networking Conference (WCNC), pages 3278--3283, Paris, France, April 2012.Google ScholarCross Ref
Index Terms
- Radio tomographic imaging and tracking of stationary and moving people via kernel distance
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