A. Küpper, Location-Based Services: Fundamentals and Operation, 2005.
DOI : 10.1002/0470092335

A. Ladd, . Bekris, L. Rudys, D. Kavraki, and . Wallach, On the feasibility of using wireless ethernet for indoor localization, IEEE Transactions on Robotics and Automation, vol.20, issue.3, pp.555-559, 2004.
DOI : 10.1109/TRA.2004.824948

Y. Zhou, C. Law, . Yl-guan, and . Chin, Indoor Elliptical Localization Based on Asynchronous UWB Range Measurement, IEEE Transactions on Instrumentation and Measurement, vol.60, issue.1, pp.248-257, 2011.
DOI : 10.1109/TIM.2010.2049185

Q. Yang and . Sj-pan, Estimating Location Using Wi-Fi, IEEE Intelligent Systems, vol.23, issue.1, p.13, 2008.
DOI : 10.1109/MIS.2008.4

S. Fang, T. Lin, and P. Lin, Location Fingerprinting In A Decorrelated Space, IEEE Transactions on Knowledge and Data Engineering, vol.20, issue.5, pp.685-691, 2008.
DOI : 10.1109/TKDE.2007.190731

S. Fang and T. Lin, Indoor Location System Based on Discriminant-Adaptive Neural Network in IEEE 802.11 Environments, IEEE Transactions on Neural Networks, vol.19, issue.11, pp.1973-1978, 2008.
DOI : 10.1109/TNN.2008.2005494

Y. Kong, Y. Kwon, and . Park, Robust localization over obstructed interferences for inbuilding wireless applications, IEEE Transactions on Consumer Electronics, vol.55, issue.1, pp.105-111, 2009.
DOI : 10.1109/TCE.2009.4814421

S. Hong, B. Kim, and D. Eom, Localization algorithm in wireless sensor networks with network mobility, IEEE Transactions on Consumer Electronics, vol.55, issue.4, pp.1921-1928, 2009.
DOI : 10.1109/TCE.2009.5373751

S. Mazuelas, . Bahillo, . Lorenzo, . Fernandez, . Lago et al., Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks, IEEE Journal of Selected Topics in Signal Processing, vol.3, issue.5, pp.821-831, 2009.
DOI : 10.1109/JSTSP.2009.2029191

V. Otsason, . Varshavsky, . Lamarca, and L. De, Accurate GSM Indoor Localization, Proceedings of the 7th International Conference on Ubiquitous Computing, pp.141-158, 2005.
DOI : 10.1007/11551201_9
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.94.3208

W. Ur-rehman, S. De-lara, and . Saroiu, CILoS, a CDMA indoor localization system, Proceedings of the 10th International Conference on Ubiquitous Computing, pp.21-24, 2008.

Y. Oussar, . Ahriz, G. Denby, and . Dreyfus, Indoor localization based on cellular telephony RSSI fingerprints containing very large numbers of carriers, EURASIP Journal on Wireless Communications and Networking, vol.2011, issue.1, p.81, 2011.
DOI : 10.1109/TRA.2004.824948

Y. Tian, . Denby, . Ahriz, . Roussel, G. Dubois et al., Practical indoor localization using ambient RF, 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp.1125-1129, 2013.
DOI : 10.1109/I2MTC.2013.6555589
URL : https://hal.archives-ouvertes.fr/hal-00961295

K. Whitehouse, C. Karlof, and D. Culler, A practical evaluation of radio signal strength for ranging-based localization, ACM SIGMOBILE Mobile Computing and Communications Review, vol.11, issue.1, pp.41-52, 2007.
DOI : 10.1145/1234822.1234829

V. Zheng, . Xiang, D. Yang, and . Shen, Transferring localization models over time, Proceedings of the 23rd National Conference on Artificial Intelligence, pp.1421-1426, 2008.

Y. Jin, W. Soh, and W. Wong, Error analysis for fingerprint-based localization, IEEE Communications Letters, vol.14, issue.5, pp.393-395, 2010.
DOI : 10.1109/LCOMM.2010.05.092152

M. Page, J. Molina, and . Gordon, The mobile economy 2013, 2013.

M. Rahnema, Overview of the GSM system and protocol architecture, IEEE Communications Magazine, vol.31, issue.4, pp.92-100, 1993.
DOI : 10.1109/35.210402

N. Cristianini and J. Shawe-taylor, Support Vector Machines and Other Kernel-Based Learning Methods, 2000.
DOI : 10.1017/CBO9780511801389

T. Joachims, Making large-scale SVM learning practical, Advances in Kernel Methods?Support Vector Learning, 1999.

C. Hsu and C. Lin, A comparison of methods for multiclass support vector machines, IEEE Trans. Neural Netw, vol.13, pp.415-425, 2002.

A. Jazwinski, Stochastic Processes and Filtering Theory (Academic, 1970.

A. Bekkali and M. Matsumoto, Bayesian sensor model for indoor localization in Ubiquitous Sensor Network, 2008 First ITU-T Kaleidoscope Academic Conference, Innovations in NGN: Future Network and Services, pp.285-292, 2008.
DOI : 10.1109/KINGN.2008.4542278

C. Chao, C. Chu, and A. Wu, Location-Constrained Particle Filter human positioning and tracking system, 2008 IEEE Workshop on Signal Processing Systems, pp.73-76, 2008.
DOI : 10.1109/SIPS.2008.4671740