Performance Comparison of UWB-Fingerprinting Positioning with RBF Neural Network and k-Nearest Neighbor in an Indoor Environment

Main Article Content

Wipassorn Winitchaikul
Jirapat Sangthong
Kannika Limpisawat
Pichaya Supanakoon
Sathaporn Promwong

Abstract

- In recent years, an indoor positioning system has been widely used in medical, industrial, public safety and transportation. In addition, its important requirement is high accuracy in dense multipath fading environments. This paper studies on indoor positioning using radial basis function (RBF) neural network and k-nearest-neighbor (k-NN) based on ultra wideband (UWB) signal. The channel transfer function was measured using vector network analyzer (VNA) at the frequency ranging from 3 GHz to 11 GHz. The path losses and the delay times of first three paths were investigated to build the fingerprints and signatures. The accuracy of this work is studied and shown in the term of cumulative distribution function (CDF). From the results, RBF neural network provides better accuracy than k-NN. Thus, RBF neural network is more suitable for an indoor positioning.

Article Details

How to Cite
[1]
W. Winitchaikul, J. Sangthong, K. Limpisawat, P. Supanakoon, and S. Promwong, “Performance Comparison of UWB-Fingerprinting Positioning with RBF Neural Network and k-Nearest Neighbor in an Indoor Environment”, JIST, vol. 3, no. 1, pp. 16–22, Jun. 2012.
Section
Research Article: Soft Computing (Detail in Scope of Journal)

References

1. L. Zwirello, M. Janson, C. Ascher and U. Schwesinger, “Localization in Industrial Halls via Ultra-Wideband Signals,” 2010 Workshop on Positioning Navigation and Communication, pp. 144–149, Mar. 2010.

2. H. Liu, H. Darabi, P. Banerjee and J. Liu, “Survey of wireless indoor positioning techniques and systems,” IEEE Transactions on Systems, Man, and Cybernetics Part C, vol. 37, no. 6, pp. 1067–1080, Nov. 2007.

3. R. Uppahad, J. Sangthong, and S. Promwong, “UWB Localization with 2-D Interpolation and K-Nearest Neighbor Based on Measurement Data,” 2011 International Symposium on Antennas and Propagation, Oct. 2011.

4. A. Toak, N. Kandil, S. Affes and S. Georges, “Neural Networks for Fingerprinting-Based indoor Localization Using Ultra-Wideband,” Journal of Communications, Vol. 4, No. 4, pp. 267-275, May 2009.

5. N. K.Bose and P.Liang, Neural Network fundamentals with graphs, algorithm, and applications, 1st ed., NY: McGraw-Hill, 1996.

6. K. Limpisawat, P. Supanakoon, S. Promwong, and J. Sangthong, “UWB Localization Measurement in an Indoor Environment,” 2010 International Workshop on Information Communication Technology, Aug. 2010.

7. H. Liu, H. Darabi, P. Banerjee and J. Liu, “Survey of wireless indoor positioning techniques and systems,” IEEE Transactions on Systems, Man, and Cybernetics Part C, vol. 37, no. 6, pp. 1067–1080, Nov. 2007.

8. Y. Gu, A. Lo and I. Niemegeers, “A survey of indoor positioning systems for wireless personal networks,” IEEE Communications Surveys and Tutorials, vol. 11, no. 1, pp. 13–32, Jan.-Mar. 2009.

9. F. Lauvene. Fundamentals of Neural Networks Architectures, Algorithms and Applications. The United States of America: Prentice – Hall, Inc, 1994.

10. W. D. Philip. Advanced Methods in Neural Computing. The United States of America: Van Nostrand Reinhold, 1993.

11. Stamatios V. Kartalopoulos. Understanding Neural Networks and Fuzzy Logic. The United States of America: IEEE Press, 1996.

12. S.Y.Kung. Digital Neural Networks. The United States of America: