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.
[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
[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
@article{"International Journal of Electrical, Electronic and Communication Sciences:60882", author = "M.Stella and M. Russo and D. Begušić", title = "GSM-Based Approach for Indoor Localization", 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.", volume = "7", number = "4", pages = "419-5", }
