This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in Pervasive Computing
September 2006 (vol. 18 no. 9)
pp. 1181-1193
In this paper, we present an algorithm for multidimensional vector regression on data that are highly uncertain and nonlinear, and then apply it to the problem of indoor location estimation in a wireless local area network (WLAN). Our aim is to obtain an accurate mapping between the signal space and the physical space without requiring too much human calibration effort. This location estimation problem has traditionally been tackled through probabilistic models trained on manually labeled data, which are expensive to obtain. In contrast, our algorithm adopts Kernel Canonical Correlation Analysis (KCCA) to build a nonlinear mapping between the signal-vector space and the physical location space by transforming data in both spaces into their canonical features. This allows the pairwise similarity of samples in both spaces to be maximally correlated using kernels. We use a Gaussian kernel to adapt to the noisy characteristics of signal strengths and a Matérn kernel to sense the changes in physical locations. By using real data collected in an 802.11 wireless LAN environment, we achieve accurate location estimation for pervasive computing while requiring a much smaller set of labeled training data than previous methods.

[1] A. Civilis, C. Jensen, and S. Pakalnis, “Techniques for Efficient Road-Network-Based Tracking of Moving Objects,” IEEE Trans. Knowledge and Data Eng., vol. 17, pp. 698-712, 2005.
[2] L. Liao, D. Fox, and H. Kautz, “Learning and Inferring Transportation Routines,” Proc. 19th Nat'l Conf. Artificial Intelligence, pp. 348-353, July 2004.
[3] J. Yin, X. Chai, and Q. Yang, “High-Level Goal Recognition in a Wireless LAN,” Proc. 19th Nat'l Conf. Artificial Intelligence, pp. 578-584, July 2004.
[4] P. Bahl and V. Padmanabhan, “RADAR: An In-Building RF-Based User Location and Tracking System,” Proc. Conf. Computer Comm., vol. 2, pp. 775-784, 2000, .
[5] C. Gentile and L. Berndt, “Robust Location Using System Dynamics and Motion Constraints,” Proc. IEEE Int'l Conf. Comm., pp. 1360-1364, June 2004.
[6] A. Ladd, K. Bekris, G. Marceau, A. Rudys, L. Kavraki, and D. Wallach, “Robotics-Based Location Sensing Using Wireless Ethernet,” Proc. Eighth ACM Int'l Conf. Mobile Computing and Networking, pp. 227-238, Sept. 2002.
[7] L. Ni, Y. Liu, Y. Lau, and A. Patil, “LANDMARC: Indoor Location Sensing Using Active RFID, ” Proc. First IEEE Int'l Conf. Pervasive Computing and Comm., pp. 407-416, Mar. 2003.
[8] M. Youssef, A. Agrawala, and U. Shankar, “WLAN Location Determination via Clustering and Probability Distributions,” Proc. First IEEE Int'l Conf. Pervasive Computing and Comm., pp. 143-150, Mar. 2003.
[9] Y. Chen, Q. Yang, J. Yin, and X. Chai, “Power-Efficient Access-Point Selection for Indoor Location Estimation,” IEEE Trans. Knowledge and Data Eng., to appear.
[10] L. Hu and D. Evans, “Localization for Mobile Sensor Networks,” Proc. 10th Ann. Int'l Conf. Mobile Computing and Networking, pp. 45-57, 2004.
[11] A. Savvides, C. Han, and M.B. Strivastava, “Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors,” Proc. Seventh Ann. Int'l Conf. Mobile Computing and Networking, pp. 166-179, 2001.
[12] Y. Gwon, R. Jain, and T. Kawahara, “Robust Indoor Location Estimation of Stationary and Mobile Users,” Proc. Conf. Computer Comm., 2004.
[13] K. Kaemarungsi and P. Krishnamurthy, “Modeling of Indoor Positioning Systems Based on Location Fingerprinting,” Proc. IEEE Int'l Conf. Computer Comm., no. 1, pp. 1013-1023, 2004.
[14] D. Maligan, E. Elnahrawy, R. Martin, W. Ju, P. Krishnan, and A. Krishnakumar, “Bayesian Indoor Positioning Systems,” Proc. Conf. Computer Comm., vol. 2, pp. 1217-1227, Mar. 2005.
[15] T. Roos, P. Myllymaki, H. Tirri, P. Misikangas, and J. Sievanen, “A Probabilistic Approach to WLAN User Location Estimation,” Int'l J. Wireless Information Networks, vol. 9, no. 3, pp. 155-164, 2002.
[16] P. Krishnan, A.S. Krishnakumar, W. Jun, C. Mallows, and S. Ganu, “A System for LEASE: Location Estimation Assisted by Stationary Emitters for Indoor RF Wireless Networks,” Proc. Conf. Computer Comm., 2004.
[17] B. Schölkopf and A. Smola, Learning with Kernels. Cambridge, Mass.: MIT Press, 2002.
[18] J.J. Pan, J.T. Kwok, Q. Yang, and Y. Chen, “Accurate and Low-Cost Location Estimation Using Kernels,” Proc. 19th Int'l Joint Conf. Artificial Intelligence, to appear.
[19] S. Ganu, A.S. Krishnakumar, and P. Krishnan, “Infrastructure-Based Location Estimation in WLAN Networks,” IEEE Wireless Comm. and Networking Conf., Mar. 2004.
[20] A. Smailagic and D. Kogan, “Location Sensing and Privacy in a Context-Aware Computing Environment,” IEEE Wireless Comm., vol. 9, no. 5, pp. 10-17, Oct. 2002.
[21] E. Bhasker, S. Brown, and W. Griswold, “Employing User Feedback for Fast, Accurate, Low-Maintenance Geolocationing,” Proc. IEEE Int'l Conf. Pervasive Computing and Comm., pp. 111-120, Mar. 2004.
[22] X. Chai and Q. Yang, “Reducing Calibration Effort for Location Estimation Using Unlabeled Samples,” Proc. Third IEEE Int'l Conf. Pervasive Computing and Comm., pp. 95-104, 2005.
[23] T. Roos, P. Myllymaki, and H. Tirri, “A Statistical Modeling Approach to Location Estimation,” IEEE Trans. Mobile Computing, vol. 1, no. 1, pp. 59-69, 2002.
[24] 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.
[25] A. Goldsmith, Wireless Comm. Cambridge: Cambridge Univ. Press, 2005.
[26] H. Hashemi, “The Indoor Radio Propagation Channel,” Proc. IEEE, vol. 81, no. 7, pp. 943-968, 1993.
[27] J. Yin, Q. Yang, and L. Ni, “Adaptive Temporal Radio Maps for Indoor Location Estimation,” Proc. Third Ann. IEEE Int'l Conf. Pervasive Computing and Comm., pp. 85-94, Mar. 2005.
[28] P. Bahl, A. Balachandran, and V. Padmanabhan, “Enhancements to the RADAR User Location and Tracking System,” Microsoft Research, technical report, Feb. 2000.
[29] D. Fox, J. Hightower, L. Liao, and D. Schulz, “Bayesian Filtering for Location Estimation,” IEEE Pervasive Computing, vol. 2, no. 3, pp. 24-33, 2003.
[30] M. Youssef and A. Agrawala, “Handling Samples Correlation in the Horus Sstem,” Proc. IEEE Int'l Conf. Computer Comm., vol. 2, pp. 7-11, Mar. 2004.
[31] C.A. Micchelli and M. Pontil, “On Learning Vector-Valued Functions,” Neural Computation, vol. 17, pp. 177-204, 2005.
[32] R.T.T. Hastie and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics, 2002.
[33] L. Breiman and J. Friedman, “Predicting Multivariate Responses in Multiple Linear Regression,” J. Royal Statistics Soc., vol. 59, pp. 3-37, 1997.
[34] L. D'Ambra and R. Lombardo, “Predicting Multivariate Responses in Non-Linear Regression,” Bull. Int'l Statistical Inst., 1999.
[35] H. Wold, “Estimation of Principal Components and Related Models by Iterative Least Squares,” Multivariate Analysis, P.R. Krishnaiaah, ed., pp. 391-420, New York: Academic Press, 1966.
[36] R. Rosipal and L.J. Trejo, “Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space,” J. Machine Learning Research, vol. 2, pp. 97-123, 2001.
[37] H. Hotelling, “Relations between Two Sets of Variates,” Biometrika, vol. 28, pp. 312-377, 1936.
[38] D. Hardoon, S. Szedmak, and J. Shawe-Taylor, “Canonical Correlation Analysis: An Overview with Application to Learning Methods,” Neural Computation, vol. 16, pp. 2639-2664, 2004.
[39] W. Press, S. Teukolsky, W. Vetterling, and B. Flannery, Numerical Recipes in C, second ed. New York: Cambridge Univ. Press, 1992.
[40] M.G. Genton, “Classes of Kernels for Machine Learning: A Statistics Perspective,” J. Machine Learning Research, vol. 2, pp. 299-312, 2001.
[41] M. Stein, Interpolation of Spatial Data Some Theory for Kriging. Springer Verlag, June 1999.
[42] A. Schwaighofer, M. Grigoras, V. Tresp, and C. Hoffmann, “GPPS: A Gaussian Process Positioning System for Cellular Networks,” Advances in Neural Information Processing Systems 16, S. Thrun, L. Saul, and B. Scholkopf, eds. Cambridge, Mass.: MIT Press, 2004.
[43] M. Brunato and R. Battiti, “Statistical Learning Theory for Location Fingerprinting in Wireless LANs,” Computer Networks, vol. 47, no. 6, pp. 825-845, 2005.
[44] N. Landwehr, M. Hall, and E. Frank, “Logistic Model Trees,” Proc. European Conf. Machine Learning, pp. 241-252, 2003.
[45] A. Haeberlen, E. Flannery, A.M. Ladd, A. Rudys, D.S. Wallach, and L.E. Kavraki, “Practical Robust Localization over Large-Scale 802.11 Wireless Networks,” Proc. 10th Ann. Int'l Conf. Mobile Computing and Networking, pp. 70-84, 2004.

Index Terms:
Location-dependent and sensitive, correlation and regression analysis, pervasive computing.
Citation:
Jeffrey Junfeng Pan, James T. Kwok, Qiang Yang, Yiqiang Chen, "Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in Pervasive Computing," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 9, pp. 1181-1193, Sept. 2006, doi:10.1109/TKDE.2006.145
Usage of this product signifies your acceptance of the Terms of Use.