GSM-Based Approach for Indoor Localization

Authors: M.Stella, M. Russo, D. Begušić

Abstract:

Ability of accurate and reliable location estimation in indoor environment is the key issue in developing great number of context aware applications and Location Based Services (LBS). Today, the most viable solution for localization is the Received Signal Strength (RSS) fingerprinting based approach using wireless local area network (WLAN). This paper presents two RSS fingerprinting based approaches – first we employ widely used WLAN based positioning as a reference system and then investigate the possibility of using GSM signals for positioning. To compare them, we developed a positioning system in real world environment, where realistic RSS measurements were collected. Multi-Layer Perceptron (MLP) neural network was used as the approximation function that maps RSS fingerprints and locations. Experimental results indicate advantage of WLAN based approach in the sense of lower localization error compared to GSM based approach, but GSM signal coverage by far outreaches WLAN coverage and for some LBS services requiring less precise accuracy our results indicate that GSM positioning can also be a viable solution.

Keywords: Indoor positioning, WLAN, GSM, RSS, location fingerprints, neural network.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1078050

References:

[1] A. H. Sayed, A. Tarighat, and N. Khajehnouri, "Network-based wireless location," IEEE Signal Processing Magazine, vol. 22, pp. 24-40, Jul 2005.
[2] S. H. Fang and T. N. Lin, "Indoor Location System Based on Discriminant-Adaptive Neural Network in IEEE 802.11 Environments," IEEE Transactions on Neural Networks, vol. 19, pp. 1973-1978, 2008.
[3] K. Pahlavan, X. R. Li, and J. P. Makela, "Indoor geolocation science and technology," IEEE Communications Magazine, vol. 40, pp. 112-118, Feb 2002.
[4] F. Gustafsson and F. Gunnarsson, "Mobile positioning using wireless networks," IEEE Signal Processing Magazine, vol. 22, pp. 41-53, Jul 2005.
[5] J. Hightower and G. Borriello, "Location systems for ubiquitous," Computer, vol. 34, pp. 57-+, Aug 2001.
[6] S. H. Fang, T. N. Lin, and P. C. Lin, "Location fingerprinting in a decorrelated space," Ieee Transactions on Knowledge and Data Engineering, vol. 20, pp. 685-691, May 2008.
[7] G. L. Sun, J. Chen, W. Guo, and K. J. R. Liu, "Signal processing techniques in network-aided positioning -
[A survey of state-of-the-art positioning designs]," IEEE Signal Processing Magazine, vol. 22, pp. 12-23, Jul 2005.
[8] M. Kjærgaard, G. Treu, and C. Linnhoff-Popien, "Zone-based RSS reporting for location fingerprinting," Pervasive Computing, pp. 316- 333, 2007.
[9] M. Brunato and R. Battiti, "Statistical learning theory for location fingerprinting in wireless LANs," Computer Networks, vol. 47, pp. 825- 845, 2005.
[10] M. A. Youssef, A. Agrawala, and A. Udaya Shankar, "WLAN location determination via clustering and probability distributions," in Pervasive Computing and Communications, 2003.(PerCom 2003). Proceedings of the First IEEE International Conference on, 2003, pp. 143-150.
[11] S. H. Fang and T. N. Lin, "A Dynamic System Approach for Radio Location Fingerprinting in Wireless Local Area Networks," IEEE Transactions on Communications, vol. 58, pp. 1020-1025, Apr 2010.
[12] S. Guolin, C. Jie, G. Wei, and K. J. R. Liu, "Signal processing techniques in network-aided positioning: a survey of state-of-the-art positioning designs," Signal Processing Magazine, IEEE, vol. 22, pp. 12- 23, 2005.
[13] ETSI, "Digital cellular telecommunications system (Phase 2+); Handover procedures, GSM 03.09 version 5.1.0," ed, 1997.
[14] P. Bahl and V. N. Padmanabhan, "RADAR: an in-building RF-based user location and tracking system," in INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, 2000, pp. 775-784 vol.2.
[15] M. Stella, M. Russo, and D. Begusic, "RF Localization in Indoor Environment," Radioengineering, vol. 21, pp. 557-567, Jun 2012.
[16] C. Nerguizian, C. Despins, and S. Affes, "Geolocation in mines with an impulse response fingerprinting technique and neural networks," IEEE Transactions on Wireless Communications, vol. 5, pp. 603-611, Mar 2006.
[17] C. Laoudias, P. Kemppi, and C. Panayiotou, "Localization using radial basis function networks and signal strength fingerprints in WLAN," in Global Telecommunications Conference, 2009. GLOBECOM 2009. IEEE, 2009, pp. 1-6.
[18] B. Kröse, B. Krose, P. van der Smagt, and P. Smagt, An introduction to neural networks: University of Amsterdam, 1996.
[19] S. Haykin, Neural networks: A comprehensive approach, 1994.
[20] NetStumbler.com. Available: http://www.netstumbler.com