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Location Fingerprinting In A Decorrelated Space
May 2008 (vol. 20 no. 5)
pp. 685-691
ASCII Text
x 
 
Shih-Hau Fang, Tsung-Nan Lin, Po-Chiang Lin, "Location Fingerprinting In A Decorrelated Space," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 5, pp. 685-691, May, 2008.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2007.190731,
author = {Shih-Hau Fang and Tsung-Nan Lin and Po-Chiang Lin},
title = {Location Fingerprinting In A Decorrelated Space},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {20},
number = {5},
issn = {1041-4347},
year = {2008},
pages = {685-691},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2007.190731},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Location Fingerprinting In A Decorrelated Space
IS - 5
SN - 1041-4347
SP685
EP691
EPD - 685-691
A1 - Shih-Hau Fang,
A1 - Tsung-Nan Lin,
A1 - Po-Chiang Lin,
PY - 2008
KW - Wireless
KW - Mobile environments
VL - 20
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
We present a novel approach to the indoor localization in wireless environments. The main contribution of this paper is four folds: (a) we show that, by projecting the measured signal into a decorrelated signal space, the accuracy is improved since the cross-correlation between each AP is reduced. (b) We demonstrate that this novel approach achieves a more efficient information compaction and provides a better scheme to reduce the online computation. The drawback of AP selection techniques is overcomed since we reduce the dimensionality by combing features. Each component in the decorrelated space is the linear combination of all APs. Therefore a more efficient mechanism is provided to utilize information of all APs while reducing the computational complexity. (c) Experimental results show that the size of training samples can be greatly reduced. That is, fewer human efforts are required for developing the system. (d) We carry out comparisons between RSS and three decorrelated spaces including Discrete Cosine Transform, principal component analysis (PCA), and independent component analysis in this paper. Two AP selection criteria proposed in literature, MaxMean and InfoGain are also compared. Testing on a realistic WLAN environment, we find that PCA achieves the best performance on the location fingerprinting task.

[1] J. Hightower and G. Borriello, “Location Systems for Ubiquitous Computing,” Computer, vol. 34, pp. 57-66, 2001.
[2] Y. Zhao, “Mobile Phone Location Determination and Its Impact on Intelligent Transportation Systems,” IEEE Trans. Intelligent Transportation Systems, vol. 1, pp. 55-64, 2000.
[3] T.S. Rappaport, J.H. Reed, and D. Woerner, “Position Location Using Wireless Communications on Highways of the Future,” IEEE Comm. Magazine, vol. 34, pp. 33-41, 1996.
[4] S. Tekinay, “Wireless Geolocation Systems and Services,” IEEE Comm. Magazine, vol. 36, p. 28, 1998.
[5] Y. Xu and W. Lee, “On Localized Prediction for Power-Efficient Object Tracking in Sensor Networks,” Distributed Computing Systems, pp. 434-439, 2003.
[6] Y. Xu, J. Winter, and W. Lee, “Prediction-Based Strategies for Energy Saving in Object Tracking Sensor Networks,” Mobile Data Management, pp.346-357, 2004.
[7] W.R. Heinzelman, A. Chandrakasan, and H. Balakrishman, “Energy-Efficient Communication Protocol for Wireless Microsensor Networks,” System Sciences, pp. 3005-3014, 2000.
[8] Y. Chen, J. Yin, X. Chai, and Q. Yang, “Power-Efficient Access-Point Selection for Indoor Location Estimation,” IEEE Trans. Knowledge and Data Eng., vol. 18, no. 7, pp. 877-888, July 2006.
[9] H. Jeong, J. Kim, and W.K. Cho, “Low-Power Multiplierless DCT Architecture Using Image Data Correlation,” IEEE Trans. Consumer Electronics, vol. 50, pp. 262-267, 2004.
[10] L. Zhang and X. Wu, “Color Demosaicking via Directional Linear Mean Square-Error Estimation,” IEEE Trans. Image Processing, vol. 14, no. 12, pp.2167-2178, 2005.
[11] K.I. Diamantaras and S.Y. Kung, Principal Component Neural Networks. John Wiley & Sons, 1996.
[12] S.H. Fang and T.N. Lin, “Indoor Localization by a Novel Probabilistic Approach,” Proc. Eighth IEEE Int'l Workshop Signal Processing Advances for Wireless Comm. (SPAWC), 2007.
[13] A. Hyvarinen, J. Karhunen, and E. Oja, Independent Component Analysis. John Wiley & Sons, 2001.
[14] J. Pan, J. Kwok, Q. Yang, and Y. Chen, “Accurate and Low-Cost Location Estimation Using Kernels,” Proc. 19th Int'l Joint Conf. Artificial Intelligence (IJCAI '05), pp. 1366-1370, 2005.
[15] J.J. Pan, J.T. 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.
[16] A.S. Tanembaum, Computer Networks. Prentice Hall, 1996.
[17] Draft Supplement to Part 11: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Medium Access Control (MAC) Enhancements for Quality of Service (QoS), IEEE 802.11e/D5.0, 2003.
[18] X. Chai and Q. Yang, “Reducing the Calibration Effort for Probabilistic Indoor Location Estimation,” IEEE Trans. Mobile Computing, vol. 6, no. 6, pp. 649-662, June 2007.
[19] A.V. Oppenheim, R.W. Schafer, and J.R. Buck, Discrete-Time Signal Processing. Prentice Hall, 1999.
[20] R. Duda, P. Hart, and D. Stork, Pattern Classification. John Wiley & Sons, 2000.
[21] R. Gray, Entropy and Information Theory. Springer-Verlag, 1990.
[22] M.A. Youssef, A. Agrawala, and A.U. Shankar, “WLAN Location Determination via Clustering and Probability Distributions,” Pervasive Computing and Comm., pp. 143-150, 2003.
[23] P. Bahl and V.N. Padmanabhan, “Radar: An In-Building RF-Based User Location and Tracking System,” Proc. IEEE INFOCOM '00, pp. 775-784, 2000.
[24] S.N. Patel, K.N. Truong, and G.D. Abowd, “Powerline Positioning: A Practical Sub-Room-Level Indoor Location System for Domestic Use,” Proc. Eighth Ann. Conf. Ubiquitous Computing (UbiComp '06), pp. 441-458, 2006.
[25] M. Brunato and R. Battiti, “Statistical Learning Theory for Location Fingerprinting in Wireless LANs,” Computer Networks, vol. 47, pp. 825-845, 2005.
[26] T.-N. Lin and P.-C. Lin, “Performance Comparison of Indoor Positioning Techniques Based on Location Fingerprinting in Wireless Networks,” Wireless Networks, Comm. and Mobile Computing, pp. 1569-1574, 2005.
[27] R. Battiti, T.L. Nhat, and A. Villani, “Location-Aware Computing: A Neural Network Model for Determining Location in Wireless LANs,” Technical Report DIT-02-0083, Dept. Information and Comm. Technology, Univ. Trento, 2002.
[28] Z.-L. Wu, C.-H. Li, J.-Y. Ng, and K.R. Leung, “Location Estimation via Support Vector Regression,” IEEE Trans. Mobile Computing, vol. 6, no. 3, pp.311-321, Mar. 2007.
[29] K. Azadeh, K.N. Plataniotis, and A.N. Venetsanopoulos, “Kernel-Based Positioning in Wireless Local Area Networks,” IEEE Trans. Mobile Computing, vol. 6, no. 6, pp. 689-705, June 2007.
[30] T. Roots, P. Myllymaki, and H. Tirri, “A Statistical Modeling Approach to Location Estimation,” IEEE Trans. Mobile Computing, vol. 1, no. 1, pp. 59-69, Jan.-Mar. 2002.
[31] M. Youssef and A. Agrawala, “Handling Samples Correlation in the Hours System,” Proc. IEEE INFOCOM '04, pp. 1023-1031, 2004.
[32] A. Hyvarinen, “Fast and Robust Fixed-Point Algorithms for Independent Component Analysis,” IEEE Trans. Neural Networks, vol. 10, pp. 626-634, 1999.
[33] M.A.Y.W. Page, http://www.cs.umd.edu/usersmoustafa/, Dept. of Computer Science, Univ. Maryland, 2001.

Index Terms:
Wireless, Mobile environments
Citation:
Shih-Hau Fang, Tsung-Nan Lin, Po-Chiang Lin, "Location Fingerprinting In A Decorrelated Space," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 5, pp. 685-691, May 2008, doi:10.1109/TKDE.2007.190731
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