Enhanced Integrated Indoor Positioning Algorithm Utilising Wi-Fi Fingerprint Technique

Authors

  • A.S. Ja’afar School of Computing and Communication Infolab21, Lancaster University, United Kingdom. Center for Telecommunication Research & Innovation (CeTRI) Faculty of Electronic & Computer Engineering Universiti Teknikal Malaysia Melaka (UTeM), Malaysia.
  • G. Markarian School of Computing and Communication Infolab21, Lancaster University, United Kingdom
  • A.A.M. Isa Center for Telecommunication Research & Innovation (CeTRI) Faculty of Electronic & Computer Engineering Universiti Teknikal Malaysia Melaka (UTeM), Malaysia.
  • N. A. Ali Center for Telecommunication Research & Innovation (CeTRI) Faculty of Electronic & Computer Engineering Universiti Teknikal Malaysia Melaka (UTeM), Malaysia.
  • M.Z.A. Abd Aziz Center for Telecommunication Research & Innovation (CeTRI) Faculty of Electronic & Computer Engineering Universiti Teknikal Malaysia Melaka (UTeM), Malaysia

Keywords:

Indoor Positioning, Wi-Fi Fingerprint, Localization,

Abstract

This paper describes an integrated positioning algorithm utilizing Wi-Fi fingerprint technique for indoor positioning. The main contribution of this work is the improvement of positioning accuracy for indoor localization even in extreme RSSI fluctuation which leads to variation of positioning error. Several layers of Wi-Fi positioning is proposed, which are based on deterministic techniques, iterative Bayesian estimation, and also Kalman filter to enhance accuracy due to noise presence. Here, accumulated accuracy is introduced where the distribution of location error is determined by estimation at each test point on path movement. The results show that the integrated algorithm enhances the estimation accuracy in several scenarios which are different Wi-Fi chipsets and movement directions. The error distribution shows an achievement of up to 65% for error less than 5m compared to the basic deterministic technique of only 45%.

References

P. D. Groves, Principle of GNSS, Inertial, and Multisensor Integrated Navigation Systems. ARTECH HOUSE INC, 2008.

P. D. Groves, L. Wang, D. Walter, H. Martin, K. Voutsis, and Z. Jiang, “The four key challenges of advanced multisensor navigation and positioning,” 2014 IEEE/ION Position, Locat. Navig. Symp. - PLANS 2014, no. May, pp. 773–792, May 2014.

M. F. M. Mahyuddin, A. A. M. Isa, M. S. I. M. Zin, A. M. A. H, Z. Manap, and M. K. Ismail, “Overview of Positioning Techniques for LTE Technology,” J. Telecommun. Electron. Comput. Eng., vol. 9, no.2, 2017.

V. Moghtadaiee, S. Lim, and A. G. Dempster, “System-Level Considerations for Signal-of-Opportunity Positioning,” Int. Symp. GPS/GNSS, pp. 1–7, 2010.

Z. Manap, A. A. M. Isa, M. H. Othman, and A. M. Darsono, “Performance Analysis of TOA-based Positioning in LTE by utilizing MIMO Feature,” J. Telecommun. Electron. Comput. Eng., vol. 9, no. 2, pp. 117–121, 2017.

H. Liu and H. Darabi, “Survey of wireless indoor positioning techniques and systems,” IEEE Trans. Syst. Man, Cybernertics, vol. 37, no. 6, pp. 1067–1080, 2007.

S. He, S. Member, S. G. Chan, and S. Member, “Wi-Fi FingerprintBased Indoor Positioning : Recent Advances and Comparisons,” vol. 18, no. 1, pp. 466–490, 2016.

B. S. -, J. H. L. -, T. L. -, and H. S. K. -, “Enhanced Weighted KNearest Neighbor Algorithm for Indoor Wi-Fi Positioning Systems,” Int. J. Networked Comput. Adv. Inf. Manag., vol. 2, pp. 15–21, 2012.

G. Lui, T. Gallagher, B. Li, A. G. Dempster, and C. Rizos, “Differences in RSSI readings made by different Wi-Fi chipsets: A limitation of WLAN localization,” in 2011 International Conference on Localization and GNSS, ICL-GNSS 2011, 2011, pp. 53–57.

D. Rodionov, D. Kolev, and K. Bushminkin, “Hybrid Positioning Technique for Indoor Environment,” in 1st Annual International Conference on Health &Medical Sciences, 2013.

J. Cheng, L. Yang, Y. Li, and W. Zhang, “Seamless outdoor/indoor navigation with WIFI/GPS aided low cost Inertial Navigation System,” Phys. Commun., Jan. 2014.

P. Bahl and V. N. Padmanabhan, “RADAR: an in-building RF-based user location and tracking system,” in Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064), 2000, vol. 2, no. c.

A. K. M. Mahtab Hossain, Y. Jin, W. S. Soh, and H. N. Van, “SSD:A robust RF location fingerprint addressing mobile devices’ heterogeneity,” IEEE Trans. Mob. Comput., vol. 12, no. 1, pp. 65 77, 2013.

S. H. Fang, C. H. Wang, S. M. Chiou, and P. Lin, “Calibration-free approaches for robust Wi-Fi positioning against device diversity: A performance comparison,” IEEE Veh. Technol. Conf., 2012.

H. Lin, Y. Zhang, M. Griss, and I. Landa, “Enhanced Indoor Locationing in a Congested Wi-Fi Environment,” in MRC-TR-2009, 2009, no. March, pp. 1–16.

J. H. Anton, Bayesian Estimation and Tracking: A Practical Guide. John Wiley & Sons, Inc. Publication, 2012.

J. Yim, J. Kim, G. Lee, and K. Shim, “Kalman filter vs. particle filter in improving K-NN indoor positioning,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6882 LNAI, no. PART 2, 2011, pp. 203–213.

R. Faragher, “Understanding the basis of the kalman filter via a simple and intuitive derivation [lecture notes],” IEEE Signal Process. Mag., vol. 29, no. 5, pp. 128–132, 2012.

P. Kim, Kalman Filter for Beginners, with MATLAB Examples. A JIN Publishing, 2011.

J. Yim, C. Park, J. Joo, and S. Jeong, “Extended Kalman Filter for wireless LAN based indoor positioning,” Decis. Support Syst., vol. 45, pp. 960–971, 2008.

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Published

2017-12-30

How to Cite

Ja’afar, A., Markarian, G., Isa, A., Ali, N. A., & Abd Aziz, M. (2017). Enhanced Integrated Indoor Positioning Algorithm Utilising Wi-Fi Fingerprint Technique. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(4), 123–129. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2440

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