An Algorithm and Data Process Scheme for Indoor Location Based on Mobile Devices

Bin Wen, Ruoshan Kong

International School of Software, Wuhan University, Wuhan, China.
State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China.

DOI: 10.4236/jcc.2014.22020                

Abstract

Limited by the sampling capacity of the mobile devices, many real-time indoor location systems have such problems as low accuracy, large variance, and non-smooth movement of the estimated position. A new positioning algorithm and a new processing method for sampled data are proposed. Firstly, a positioning algorithm is designed based on the cluster-based nearest neighbour or probability. Secondly, a weighted average method with sliding window is used to process the sampled data as to overcome the mobile devices’ weak capability of signal sampling. Experimental results show that, for the general mobile devices, the accuracy of indoor position estimation increases from 56.5% to 76.6% for a 2-meter precision, and from 77.4% to 90.9% for a 3-meter precision. Therefore, the proposed methods can significantly and stably improve the positioning accuracy.

Keywords

Indoor Positioning; Wireless LAN; Fingerprint Map; Real-time Positioning

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	M. Weiser, “Some Computer Science Issues in Ubiquitous Computing,” Communications of the ACM, Vol. 36, No. 7, 1993, pp. 75-84. http://dx.doi.org/10.1145/159544.159617
[2]	M. Hazas, J. Scott and J. Krumm, “Location-Aware Computing Comes of Age,” Computer, Vol. 37, No. 2, 2004, pp. 95-97. http://dx.doi.org/10.1109/MC.2004.1266301
[3]	R. Want, A. Hopper, V. Falcao and J. Gibbons, “The Active Badge Location System,” ACM Transactions on Information Systems, Vol. 10, No. 1, 1992, pp. 91-102. http://dx.doi.org/10.1145/128756.128759
[4]	R. Casas, “BLUPS: Bluetooth and Ultrasounds Positioning System,” Doctoral Dissertation, University of Zaragoza, 2004.
[5]	L. M. Ni, Y. Liu, Y. C. Lau and A. P. Patil, “Landmarc: Indoor Location Sensing Using Active RFID,” Pervasive Computing and Communications, 2003, pp. 407-415.
[6]	P. Bahl and V. N. Padmanabhan, “RADAR: An In-Building RF-Based User Location and Tracking System,” IEEE INFOCOM 2000 Conference, 2000, pp. 775-784.
[7]	M. Youssef and A. Agrawala, “The Horus WLAN Location Determination System,” Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services, 6-8 June 2005, pp. 205-218.
[8]	L. Doherty, L. Ghaoui and K. Pister, “Convex Position Estimation in Wireless Sensor Networks,” IEEE INFOCOM 2001 Conference, 2001, pp. 1655-1663.
[9]	A. Gunther and Ch. Hoene, “Measuring Round Trip Times to Determine the Distance between WLAN Nodes,” Proceedings of Networking, Waterloo, May 2005.
[10]	R. O. Duda and P. E. Hart, “Pattern Classification,” 2nd Edition, John Wiley, New York, 2000.
[11]	M. Youssef and A. Agrawala, “On the Optimality of WLAN Location Determination Systems,” The Communication Network sand Distributed Systems Modeling and Simulation Conference, 18-24 January 2004.
[12]	A. S. Krishnakumar and P. Krishnan, “The Theory and Practice of Signal Strength-Based Location Estimation,” Collaborative Computing: Networking, Applications and Worksharing, San Jose, 2005.
[13]	B. Wen, “An Improved Method Used in Indoor Location Based on Signal Similarity Analysis and Adaptive Algorithms Selection,” Wireless Communications, Networking and Mobile Computing 2012, 21-23 September 2012. pp. 1-6.