Indoor Positioning with Wireless Local Area Networks (WLAN)
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Indoor Positioning System
tional Carnahan Conference on Security Technology, vol.1, pp. 106–116, Ottawa, Ont., Canada (1993) Werb, J., Lanzl, C.: Designing a positioning system for finding things and people indoors. IEEE Spectrum 35, 71–78 (1998) Ladd, A.M., Bekris, K.E., Marceau, G., Rudys, A., Wallach, D.S., Kavraki, L.E.: Using Wireless Ethernet for Localization. IROS, Lausanne (2002) Bahl, P., Padmanabhan, V.N.: RADAR: An in-building RF-based user location and tracking system. IEEE Infocom 2, 775–784 (2000) Kay, S.M.: Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice Hall, Upper Saddle River, NY (1993) Doucet, A., de Freitas, N., Gordon, N. (eds.): Sequential Monte Carlo in Practice. Springer-Verlag, New York, NY (2001) Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50, 174–188 (2002) Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U.: Particle filters for positioning, navigation and tracking. IEEE Trans. Signal Process. 50, 425–437 (2002) Fox, D., Hightower, J., Kauz, H., Liao, L., Patterson, D.: Bayesian Techniques for Location Estimation. In: Proceedings of the Workshop on Location-Aware Computing (UBICOMP Conference), Seattle, WA, October 2003 Hall, D.L.: Mathematical Techniques in Multisensor Data Fusion. Artech, Boston, MA (1992) Cui, N., Hong, L., Layne, J.R. A comparison of nonlinear filtering approaches with an application to ground target tracking. Signal Process. 85, 1469–1492 (2005) Roos, T., Myllymaki, P., Tirri, H., Misikangas, P., Sievanen, J.: A Probabilistic Approach to WLAN User Location Estimation. Int. J. Wirel. Inf. Netw. 9(3), 155–164 (2002) Roos, T., Myllymäki, P., Tirri, H.: A statistical modeling approach to location estimation. IEEE Trans. Mobile Comput. 1, 59–69 (2002) Bohn, J., Vogt, H.: Robust Probabilistic Positioning Based on High-Level Sensor Fusion and Map Knowledge. Tech. Rep. 421. Institute for Pervasive Computing, Distributed Systems Group, Swiss Federal Institute of Technology (ETH), Zurich (2003) Reid, D.B.: An algorithm for tracking multiple targets. IEEE Trans. Automat. Control 24, 843–854 (1979) Bar-Shalom, Y., Fortmann, T.E.: Tracking and data association. Mathematics in Science and Engineering Series 179. Academic, San Diego (1988) Oh, S., Russell, S., Sastry, S.: Markov chain Monte Carlo data association for general multiple-target tracking problems. In: Proceedings of the 43rd IEEE Conference on Decision and Control, Paradise Island, Bahamas (2004) Hue, C., Le Cadre, J.-P., Perez, P.: Posterior Cramer-Rao bounds for multi-target tracking. IEEE Trans. Aerospace Electron. Syst. 42(1), 37–49 (2006) Ramadurai, V., Sichitiu, M.L.: Localization in wireless sensor networks: a probabilistic approach. Proc. of the 2003 International Conference on Wireless Networks (ICWN2003), vol.1, pp. 275–281, Las Vegas, NV, Jan. (2003)
Indoor Positioning System
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