The Community for Technology Leaders
RSS Icon
Issue No.03 - March (2010 vol.9)
pp: 405-419
Azadeh Kushki , University of Toronto, Toronto
Konstantinos N. Plataniotis , University of Toronto, Toronto
Anastasios N. Venetsanopoulos , University of Toronto, Toronto
Indoor positioning is an enabling technology for delivery of location-based services in mobile computing environments. This paper proposes a positioning solution using received signal strength in indoor Wireless Local Area Networks. In this application, an explicit measurement equation and the corresponding noise statistics are unknown because of the complexity of the indoor propagation channel. To address these challenges, we introduce a new state-space Bayesian filter: the Nonparametric Information (NI) filter. This filter effectively tracks motion in situations where the Kalman filter and its variants are inapplicable, while maintaining a computational complexity comparable to that of the Kalman filter. To deal with the noisy nature of the indoor propagation environment, the NI filter is used in the design of an intelligent dynamic WLAN tracking system. The system anticipates future position values and adapts its sensing and estimation parameters accordingly. Our experimental results conducted on measurements from a real office environment indicate that the combination of the intelligent design and the NI filter results in significant improvements over the Kalman and particle filters.
Mobility, location verification, location-dependent and sensitive mobile applications, nonparametric statistics, support services for mobile computing.
Azadeh Kushki, Konstantinos N. Plataniotis, Anastasios N. Venetsanopoulos, "Intelligent Dynamic Radio Tracking in Indoor Wireless Local Area Networks", IEEE Transactions on Mobile Computing, vol.9, no. 3, pp. 405-419, March 2010, doi:10.1109/TMC.2009.141
[1] A. Roy, S. Das, and K. Basu, “A Predictive Framework for Location-Aware Resource Management in Smart Homes,” IEEE Trans. Mobile Computing, vol. 6, no. 11, pp. 1270-1283, Nov. 2007.
[2] M. Hazas, J. Scott, and J. Krumm, “Location-Aware Computing Comes of Age,” Computer, vol. 37, no. 2, pp. 95-97, Feb. 2004.
[3] K.-T. Feng, C.-L. Chen, and C.-H. Chen, “Gale: An Enhanced Geometry-Assisted Location Estimation Algorithm for NLOS Environments,” IEEE Trans. Mobile Computing, vol. 7, no. 2, pp.199-213, Feb. 2008.
[4] M.B. Kjærgaard, “A Taxonomy for Radio Location Fingerprinting,” Lecture Notes in Computer Science, pp. 139-156, Springer, 2007.
[5] A. Kushki, K. Plataniotis, and A. Venetsanopoulos, “Kernel-Based Positioning in Wireless Local Area Networks,” IEEE Trans. Mobile Computing, vol. 6, no. 6, pp. 689-705, June 2007.
[6] Y. Jie, Y. Qiang, and N. Lionel, “Learning Adaptive Temporal Radio Maps for Signal-Strength-Based Location Estimation,” IEEE Trans. Mobile Computing, vol. 7, no. 7, pp. 869-883, July 2008.
[7] M. Youssef and A. Agrawala, “The Horus WLAN Location Determination System,” Proc. Third Int'l Conf. Mobile Systems, Applications, and Services, pp. 205-218, 2005.
[8] P. Bahl and V. Padmanabhan, “RADAR: An In-Building RF-Based User Location and Tracking System,” Proc. IEEE INFOCOM, vol. 2, pp. 775-784, 2000.
[9] K. Kaemarungsi and P. Krishnamurthy, “Properties of Indoor Received Signal Strength For WLAN Location Fingerprinting,” Proc. First Ann. Int'l Conf. Mobile and Ubiquitous Systems: Networking and Services (MOBIQUITOUS), pp. 14-23, 2004.
[10] Y. Chen, Q. Yang, J. Yin, and X. Chai, “Power-Efficient Access-Point Selection for Indoor Location Estimation,” IEEE Trans. Knowledge and Data Eng., vol. 18, no. 7, pp. 877-888, July 2006.
[11] J. Pan, J. Kwok, Q. Yang, and Y. Chen, “Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in Pervasive Computing,” IEEE Trans. Knowledge and Data Eng., vol. 18, no. 9, pp. 1181-1193, Sept. 2006.
[12] P. Prasithsangaree, P. Krishnamurthy, and P. Chrysanthis, “On Indoor Position Location with Wireless LANs,” Proc. 13th IEEE Int'l Symp. Personal, Indoor, and Mobile Radio Comm., vol. 2, pp. 720-724, 2002.
[13] A. Kushki, K. Plataniotis, and A.N. Venetsanopoulos, “Location Tracking in Wireless Local Area Networks with Adaptive Radio Maps,” Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing (ICASSP), vol. 5, pp. 741-744, 2006.
[14] A. Ladd, K. Bekris, A. Rudys, D. Wallach, and L. Kavraki, “On the Feasibility of Using Wireless Ethernet for Indoor Localization,” IEEE Trans. Robotics and Automation, vol. 20, no. 3, pp. 555-559, June 2004.
[15] F. Evennou, F. Marx, and E. Novakov, “Map-Aided Indoor Mobile Positioning System Using Particle Filter,” Proc. IEEE Wireless Comm. and Networking Conf. (WCNC), vol. 4, pp. 2490-2494, 2005.
[16] H. Wang, A. Szabo, J. Bamberger, D. Brunn, and U. Hanebeck, “Performance Comparison of Nonlinear Filters for Indoor WLAN Positioning,” Proc. Int'l Conf. Information Fusion, 2008.
[17] A. Paul and E. Wan, “Wi-Fi Based Indoor Localization and Tracking Using Sigma-Point Kalman Filtering Methods,” Proc. IEEE/ION Position, Location and Navigation Symp., pp. 646-659, 2008.
[18] S. Haykin, “Cognitive Dynamic Systems,” Proc. IEEE, vol. 94, no. 11, pp. 1910-1911, 2006.
[19] S. Haykin, “Cognitive Dynamic Systems,” Proc. IEEE Conf. Acoustics, Speech and Signal Processing (ICASSP), vol. 4, pp. 1369-1372, 2007.
[20] S. Haykin, “Cognitive Radio: Brain-Empowered Wireless Communications,” IEEE J. Selected Areas Comm., vol. 23, no. 2, pp. 201-220, 2005.
[21] S. Haykin, “Cognitive Radar: A Way of the Future,” IEEE Signal Processing Magazine, vol. 23, no. 1, pp. 30-40, 2006.
[22] R. Singh, L. Macchi, C. Regazzoni, and K. Plataniotis, “A Statistical Modelling Based Location Determination Method Using Fusion in Wlan,” Proc. Int'l Workshop Wireless Ad-Hoc Networks, 2005.
[23] A. Goldsmith, Wireless Communications. Cambridge Univ. Press, 2005.
[24] P. Krishnan, A. Krishnakumar, W.-H. Ju, C. Mallows, and S. Ganu, “A System for LEASE: Location Estimation Assisted by Stationary Emitters for Indoor RF Wireless Networks,” Proc. IEEE INFOCOM, vol. 2, pp. 1001-1011, 2004.
[25] S.-H. Fang, T.-N. Lin, and K.-C. Lee, “A Novel Algorithm for Multipath Fingerprinting in Indoor WLAN Environments,” IEEE Trans. Wireless Comm., vol. 7, no. 9, pp. 3579-3588, Sept. 2008.
[26] M. Youssef, A. Agrawala, and A.U. Shankar, “WLAN Location Determination via Clustering and Probability Distributions,” Proc. First IEEE Int'l Conf. Pervasive Computing and Comm., pp. 143-150, 2003.
[27] A. Kushki, K. Plataniotis, and A. Venetsanopoulos, “Sensor Selection for Mitigation of RSS-Based Attacks in Wireless Local Area Network Positioning,” to be published in Proc. IEEE Int'l Conf. Acoustics, Speech and Signal Processing (ICASSP), 2008.
[28] S.-H. Fang, T.-N. Lin, and P.-C. Lin, “Location Fingerprinting in a Decorrelated Space,” IEEE Trans. Knowledge and Data Eng., vol. 20, no. 5, pp. 685-691, May 2008.
[29] S.-H. Fang and T.-N. Lin, “Indoor Location System Based on Discriminant-Adaptive Neural Network in IEEE 802.11 Environments,” IEEE Trans. Neural Networks, vol. 19, no. 11, pp. 1973-1978, Nov. 2008.
[30] R. Battiti, M. Brunato, and A. Villani, “Statistical Learning Theory for Location Fingerprinting in Wireless LANs,” Technical Report DIT-020086, Dipartimento di Informatica e Telecomunicazioni, Universita di Trento, Oct. 2002.
[31] M. Borenovic, A. Neskovic, D. Budimir, and L. Zezelj, “Utilizing Artificial Neural Networks for WLAN Positioning,” Proc. Symp. Personal, Indoor and Mobile Radio Comm. (PIMRC), 2008.
[32] S. Haykin, Adaptive Filter Theory. Prentice Hall, 2002.
[33] T.K. Moon and W.C. Stirling, Mathematical Methods and Algorithms for Signal Processing. Prentice Hall, 2000.
[34] I. Guvenc, C. Abdallah, R. Jordan, and O. Dedeoglu, “Enhancements to RSS Based Indoor Tracking Systems Using Kalman Filters,” Proc. Int'l Signal Processing Conf. and Global Signal Processing Expo, 2003.
[35] Y.-S. Chiou, C.-L. Wang, and S.-C. Yeh, “An Adaptive Location Estimator Based on Kalman Filtering for Dynamic Indoor Environments,” Proc. IEEE Vehicular Technology Conf. (VTC), pp.1-5, 2006.
[36] A.F. Cattoni, A. Dore, and C.S. Regazzoni, “Video-Radio Fusion Approach for Target Tracking in Smart Spaces,” Proc. Int'l Conf. Information Fusion, 2007.
[37] A. Dore, A.F. Cattoni, and C.S. Regazzoni, “A Particle Filter Based Fusion Framework for Video-Radio Tracking in Smart Spaces,” Proc. IEEE Conf. Advanced Video and Signal Based Surveillance (AVSS), pp. 99-104, 2007.
[38] C. Gentile and L. Klein-Berndt, “Robust Location Using System Dynamics and Motion Constraints,” Proc. IEEE Int'l Conf. Comm., vol. 3, pp. 1360-1364, 2004.
[39] A.M. Ladd, K.E. Bekris, A. Rudys, L.E. Kavraki, and D.S. Wallach, “Robotics-Based Location Sensing Using Wireless Ethernet,” Wireless Networks, vol. 11, no. 1, pp. 189-204, 2005.
[40] G. Antonini, “A Discrete Choice Modeling Framework for Pedestrian Walking Behavior with Application to Human Tracking in Video Sequences,” PhD dissertation, Ècole Polytechnique Fèdèrale de Lausanne, 2006.
[41] B.-F. Wu, C.-L. Jen, and K.-C. Chang, “Neural Fuzzy Based Indoor Localization by Kalman Filtering with Propagation Channel Modeling,” Proc. IEEE Int'l Conf. Systems, Man and Cybernetics, pp. 812-817, 2007.
[42] D.W. Scott, Multivariate Density Estimation. John Wiley and Sons, 1992.
[43] M. McGuire, K. Plataniotis, and A. Venetsanopoulos, “Data Fusion of Power and Time Measurements for Mobile Terminal Location,” IEEE Trans. Mobile Computing, vol. 4, no. 2, pp. 142-153, Mar./Apr. 2005.
[44] K. Kaemarungsi and P. Krishnamurthy, “Modeling of Indoor Positioning Systems Based on Location Fingerprinting,” Proc. IEEE INFOCOM, vol. 2, pp. 1012-1022, 2004.
[45] M. McGuire and K. Plataniotis, “Dynamic Model-Based Filtering for Mobile Terminal Location Estimation,” IEEE Trans. Vehicular Technology, vol. 52, no. 4, pp. 1012-1031, July 2003.
[46] B. Silverman, Density Estimation for Statistics and Data Analysis. Chapman and Hall, 1986.
[47] A. Kushki, “A Cognitive Radio Tracking System for Indoor Environments,” PhD dissertation, Univ. of Toronto, 2008.
[48] Y. Bar-Shalom, X.-R. Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation. Wiley, 2001.
[49] J. Manyika and H.F. Durrant-Whyte, Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach. Ellis Horwood, 1994.
[50] B. Anderson and J. Moore, Optimal Filtering. Dover Publications, 2005.
[51] A.G.O. Mutambara, Decentralized Estimation and Control for Multisensor Systems. CRC Press, 1998.
[52] Y. Ho and R. Lee, “A Bayesian Approach to Problems in Stochastic Estimation and Control,” IEEE Trans. Automatic Control, vol. 9, no. 4, pp. 333-339, Oct. 1964.
[53] H.L.V. Trees, Detection, Estimation, and Modulation Theory. John Wiley & Sons, Inc., 2001.
[54] R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, second ed. Wiley, 2001.
[55] J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge Univ. Press, 2004.
[56] Z. Xiang, S. Song, J. Chen, H. Wang, J. Huang, and X. Gao, “A Wireless LAN-Based Indoor Positioning Technology,” IBM J. Research and Development, vol. 48, nos. 5/6, pp. 617-626, 2004.
4 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool