Petteri Nurmi
ABSTRACT
We propose a grid-based GSM positioning algorithm that can be deployed entirely on mobile devices. The algorithm uses Gaussian distributions to model signal intensity variations within each grid cell. Position estimates are calculated by combining a probabilistic centroid algorithm with particle filtering. In addition to presenting the positioning algorithm, we describe methods that can be used to create, update and maintain radio maps on a mobile device. We have implemented the positioning algorithm on Nokia S60 and Nokia N900 devices and we evaluate the algorithm using a combination of offline and real world tests. The results indicate that the accuracy of our method is comparable to state-of-the-art methods, while at the same time having significantly smaller storage requirements.
- }}T. F. Chan, G. H. Golub, and R. J. LeVeque. Algorithms for computing the sample variance: Analysis and recommendations. The American Statistician, 37(3):242{247, 1983.Google Scholar
- }}M. Y. Chen, T. Sohn, D. Chmelev, D. Hahnel, J. Hightower, J. Hughes, A. LaMarca, F. Potter, I. E. Smith, and A. Varshavsky. Practical metropolitan-scale positioning for GSM phones. In Proceedings of the 8th International Conference on Ubiquitous Computing (UbiComp), volume 4206 of Lecture Notes in Computer Science, pages 225--242. Springer, 2006. Google ScholarDigital Library
- }}C. Drane, M. Macnaughtan, and C. Scott. Positioning GSM telephones. Communications Magazine, IEEE, 36(4):46--54, 59, 1998. Google ScholarDigital Library
- }}B. Ferris, D. Hahnel, and D. Fox. Gaussian processes for signal strength-based location estimation. In Robotics: Science and Systems. The MIT Press, 2006.Google Scholar
- }}A. Haeberlen, E. Flannery, A. M. Ladd, A. Rudys, D. S. Wallach, and L. E. Kavraki. Practical robust localization over large-scale 802.11 wireless networks. In Proceedings of the 10th annual international conference on Mobile computing and networking (MobiCom), pages 70--84. ACM, 2004. Google ScholarDigital Library
- }}J. Hightower and G. Borriello. Particle Filters for location estimation in ubiquitous computing: A case study. In Proceedings of the 6th International Conference on Ubiquitous Computing (Ubicomp), pages 88--106. Springer, 2006.Google Scholar
- }}Juniper Research. Mobile location based services: Applications, forecasts & opportunities 2009--2014. Research report, March 2010.Google Scholar
- }}J. Krumm and E. Horvitz. LOCADIO: Inferring motion and location from Wi-Fi signal strenghts. In Proceedings of the 1st International Conference on Mobile and Ubiquitous Systems (Mobiquitous), pages 4--14. IEEE, 2004.Google ScholarCross Ref
- }}J. Krumm and J. Platt. Minimizing calibration effort for an indoor 802.11 device location measurement system. MSR-TR-2003-82, Microsoft Research, Seattle, WA, 2003.Google Scholar
- }}J. Kukkonen, E. Lagerspetz, P. Nurmi, and M. Andersson. BeTelGeuse: A platform for gathering and processing situational data. IEEE Pervasive Computing, 8(2):49--56, 2009. Google ScholarDigital Library
- }}H. Laitinen, J. Lahteenmaki, and T. Nordstrom. Database correlation method for GSM location. In Proceedings of the 53rd IEEE Vehicular Technology Conference (VTC). IEEE, 2001.Google ScholarCross Ref
- }}A. LaMarca, Y. Chawathe, S. Consolvo, J. Hightower, I. E. Smith, J. Scott, T. Sohn, J. Howard, J. Hughes, F. Potter, J. Tabert, P. Powledge, G. Borriello, and B. N. Schilit. Place Lab: Device positioning using radio beacons in the wild. In Proceedings of 3rd International Conference on Pervasive Computing (PERVASIVE), volume 3468, pages 116--133. Springer, 2005. Google ScholarDigital Library
- }}L. Liao, D. Fox, and H. Kautz. Extracting places and activities from GPS traces using hierarchical conditional random fields. International Journal of Robotics Research, 26(1):119--134, 2007. Google ScholarDigital Library
- }}V. Otsason, A. Varshavsky, A. LaMarca, and E. de Lara. Accurate GSM indoor localization. In Proceedings of the 7th International Conference on Ubiquitous Computing (UbiComp), volume 3660 of Lecture Notes in Computer Science, pages 141--158. Springer, 2005. Google ScholarDigital Library
- }}T. Roos, P. Myllymaki, and H. Tirri. A statistical modeling approach to location estimation. IEEE Transactions on Mobile Computing, 1(1):59--69, 2002. Google ScholarDigital Library
- }}T. Roos, P. Myllymaki, H. Tirri, P. Misikangas, and J. Sievanen. A probabilistic approach to WLAN user location estimation. International Journal of Wireless Information Networks, 9(3):155--164, 2002.Google ScholarCross Ref
- }}A. Schwaighofer, M. Grigoras, V. Tresp, and C. Hoffmann. GPPS: A Gaussian process positioning system for cellular networks. In Advances in Neural Information Processing Systems 16. MIT Press, 2003.Google Scholar
- }}T. Sohn, A. Varshavsky, A. LaMarca, M. Y. Chen, T. Choudhury, I. Smith, S. Consolvo, J. Hightower, W. G. Griswold, and E. de Lara. Mobility detection using everyday GSM traces. In Proceedings of the 8th International Conference on Ubiquitous Computing (Ubicomp), pages 212--224, 2006. Google ScholarDigital Library
- }}S. Thrun, D. Fox, W. Burgard, and F. Dellaert. Robust Monte Carlo localization for Mobile Robots. Artificial Intelligence, 128:99--141, 2001. Google ScholarDigital Library
- }}A. Varshavsky, E. de Lara, J. Hightower, A. LaMarca, and V. Otsason. GSM indoor localization. Pervasive and Mobile Computing, 3:698--720, 2007. Google ScholarDigital Library
- }}A. Varshavsky, A. LaMarca, J. Hightower, and E. de Lara. The SkyLoc floor localization system. In Proceedings of the 5th Annual IEEE International Conference on Pervasive Computing and Communications (PerCom), pages 125--134. IEEE, 2007. Google ScholarDigital Library
- }}T. Vincenty. Direct and Inverse Solutions of Geodesics on the Ellipsoid with Application of Nested Equations. Survey Review, 23(176):88--93, 1975.Google ScholarCross Ref
- }}Z. Wu, C. Li, J. K.-Y. Ng, and K. R. Leung. Location estimation via support vector regression. IEEE Transactions on Mobile Computing, 6(3):311--321, 2007.. Google ScholarDigital Library
Index Terms
- A grid-based algorithm for on-device GSM positioning
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