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IJAT Vol.11 No.3 pp. 459-471
doi: 10.20965/ijat.2017.p0459
(2017)

Technical Paper:

A Novel Automated Construction Method of Signal Fingerprint Database for Mobile Robot Wireless Positioning System

Abdul Halim Ismail*,***,†, Yuki Mizushiri*, Ryosuke Tasaki*, Hideo Kitagawa**, Takanori Miyoshi*, and Kazuhiko Terashima*

*System and Control Laboratory, Department of Mechanical Engineering, Toyohashi University of Technology
1-1 Hibarigaoka Tenpaku-cho Toyohashi, Aichi 441-8580, Japan

Corresponding author

**Department of Electronic Control Engineering, National Institute of Technology, Gifu College, Gifu, Japan

***School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Arau Perlis, Malaysia

Received:
September 1, 2016
Accepted:
December 6, 2016
Online released:
April 28, 2017
Published:
May 5, 2017
Keywords:
wireless positioning system, fingerprinting database, interpolation, signal propagation, modified Shepard’s method
Abstract

The indoor wireless positioning system for a mobile robot employing fingerprinting technique is made in two phase, offline, i.e., collecting reference data for database, and online, i.e., matching the unknown data to those in the database. It is commonly known that the positioning accuracy is increased with the larger number of the reference locations. This has made the offline phase a tedious works, where laborious efforts are needed to construct the database. Thus, automatic database construction is desired in order to minimize the human efforts. This paper described the Signal Propagated Modified Shepard’s Method (SP-MSM) to construct the database by means of interpolating the missing wireless data using for mobile robot application. By introducing the selection probability, reference locations are identified and database is constructed. We found that over all 64 test locations, the proposed SP-MSM method outperform other interpolation method at 52% locations. In addition, the usage of low pass filter has greatly suppressed the fluctuation problem caused by unpredicted behavior of the wireless signal.

References
  1. [1] T. He, M. Bando, M. Guarnieri, and S. Hirose, “The Development of an Autonomous Robot System for Patrolling in Multi-Floor Structured Environment,” Int. Journal of Automation Technology, Vol.6, No.1, pp. 13-21, 2012.
  2. [2] C. Ververidis and G. C. Polyzos, “Mobile Marketing Using a Location Based Service,” Communication, 2002.
  3. [3] P. Bahl and V. N. Padmanabhan, “RADAR: An In-Building RF-Based User Location and Tracking System,” Proc. IEEE INFOCOM, pp. 775-784, 2000.
  4. [4] B. Li, J. Salter, A. Dempster, and C. Rizos, “Indoor Positioning Techniques Based on Wireless LAN,” First IEEE Int. Conf. on Wireless Broadband and Ultra Wideband Communications, pp. 13-16, 2006.
  5. [5] T. Rappaport, “Wireless Communications: Principles and Practice,” 2nd ed., Upper Saddle River, NJ, USA: Prentice Hall PTR, 2001.
  6. [6] S. Donald, “A Two-Dimensional Interpolation Function For Irregularly-Spaced Data,” Proc. of the 23rd ACM National Conf., 1968.
  7. [7] Li, Binghao et al., “Method for Yielding a Database of Location Fingerprints in WLAN,” IEEE Proc. Communications, Vol.152, No.5, 2005.
  8. [8] S.-S. Jan, S.-J. Yeh, and Y.-W. Liu, “Received Signal Strength Database Interpolation by Kriging for a Wi-Fi Indoor Positioning System,” Sensors, Vol.15, No.9, 2015.
  9. [9] C. Liu et al., “A Kriging Algorithm for Location Fingerprinting Based on Received Signal Strength,” IEEE Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2015.
  10. [10] E. I. Adegoke, R. M. Edwards, W. Whittow, and A. Bindel, “Evaluating 2-D Grid Interpolation Techniques for Predicting Ambient RF Power Density in Automobile Factories,” 10th European Conf. on Antennas and Propagation (EuCAP), pp. 1-4, 2016.
  11. [11] G. Matheron, “Principle of Geostatistics,” Economic Geology, Vol.58, No.8, pp. 1246-1266, 1963.
  12. [12] W. Schwanghart and N. J. Kuhn, “TopoToolbox: A Set of Matlab Functions for Topographic Analysis,” Environmental Modelling & Software, Vol.25, No.6, pp. 770-781, 2010.
  13. [13] B. Li, J. Salter, A. Dempster, and C. Rizos, “Indoor Positioning Techniques Based on Wireless LAN,” First IEEE Int. Conf. on Wireless Broadband and Ultra Wideband Communications, pp. 13-16, 2006.
  14. [14] J. Li and A. D. Heap, “A Review of Comparative Studies of Spatial Interpolation Methods in Environmental Sciences: Performance and Impact Factors,” Ecological Informatics, Vol.6, No.3, pp. 228-241, 2011.
  15. [15] R. J. Renka, “Multivariate Interpolation of Large Sets of Scattered Data,” ACM Trans. on Mathematical Software, Vol.14, No.2, pp. 139-148, 1988.
  16. [16] A. H. Ismail, R. Tasaki, H. Kitagawa, and K. Terashima, “WiFi RSS Fingerprint Database Construction for Mobile Robot Indoor Positioning System,” Proc. IEEE Int. Conf. on Systems, Man, and Cybernetics, pp. 1561-1566, 2016.
  17. [17] M. Lee and D. Han, “Voronoi Tessellation based Interpolation Method for Wi-Fi Radio Map Construction,” IEEE Communications Letters, Vol.16, No.3, pp. 404-407, 2012.
  18. [18] J. A. Nelder and R. Mead, “A Simplex Method for Function Minimization,” The Computer J., Vol.7, No.4, pp. 308-313, 1965.
  19. [19] K. Basso, P. R. Ávila Zingano, and C. M. Dal Sasso Freitas, “Interpolation of Scattered Data: Investigating Alternatives for the Modified Shepard Method,” IEEE XII Brazilian Symposium on Computer Graphics and Image Processing, 1999.
  20. [20] A. H. Ismail, R. Tasaki, H. Kitagawa, and K. Terashima, “Optimum Placement of Wireless Access Point for Mobile Robot Positioning in an Indoor Environment,” J. of Robotics and Mechatronic, Vol.28, No.2, pp. 162-172, 2016.
  21. [21] T. Thewan, A. H. Ismail, M. Panya, and K. Terashima, “Assessment of WiFi RSS Using Design of Experiment for Mobile Robot Wireless Positioning System,” Int. Conf. on Information Fusion, pp. 855-860, 2016.
  22. [22] Steffen, “Homedale::Wi-Fi / WLAN Monitor.” [Online].
    Available at: http://thesz.diecru.eu/content/homedale.php [Accessed December 8, 2015]

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Last updated on Nov. 10, 2017