GSM-Based Approach for Indoor Localization

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.

[1]  Y. Ebihara.  Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[2]  Moustafa Youssef,et al.  WLAN location determination via clustering and probability distributions , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[3]  Shih-Hau Fang,et al.  Indoor Location System Based on Discriminant-Adaptive Neural Network in IEEE 802.11 Environments , 2008, IEEE Transactions on Neural Networks.

[4]  K.J.R. Liu,et al.  Signal processing techniques in network-aided positioning: a survey of state-of-the-art positioning designs , 2005, IEEE Signal Processing Magazine.

[5]  Christoforos Panayiotou,et al.  Localization Using Radial Basis Function Networks and Signal Strength Fingerprints in WLAN , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[6]  Juha-Pekka Makela,et al.  Indoor geolocation science and technology , 2002, IEEE Commun. Mag..

[7]  Dinko Begusic,et al.  RF Localization in Indoor Environment , 2012 .

[8]  Shih-Hau Fang,et al.  Location Fingerprinting In A Decorrelated Space , 2008, IEEE Transactions on Knowledge and Data Engineering.

[9]  Ali H. Sayed,et al.  Network-based wireless location , 2005 .

[10]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[11]  Shih-Hau Fang,et al.  A dynamic system approach for radio location fingerprinting in wireless local area networks , 2010, IEEE Transactions on Communications.

[12]  Fredrik Gustafsson,et al.  Mobile Positioning Using Wireless Networks , 2005 .

[13]  Mikkel Baun Kjærgaard,et al.  Zone-Based RSS Reporting for Location Fingerprinting , 2007, Pervasive.

[14]  Etsi Smg.  Digital cellular telecommunications system (Phase 2+); Handover procedures (GSM 03.09 version 5.1.0) , 1997 .

[15]  Mauro Brunato,et al.  Statistical learning theory for location fingerprinting in wireless LANs , 2005, Comput. Networks.

[16]  Charles L. Despins,et al.  Geolocation in mines with an impulse response fingerprinting technique and neural networks , 2006, IEEE Transactions on Wireless Communications.

[17]  Gaetano Borriello,et al.  Location Systems for Ubiquitous Computing , 2001, Computer.