ABSTRACT
Location fingerprinting is a common approach to indoor localization. For good accuracy, the training set of sample fingerprints, each mapping a fingerprint to a location, should be sufficiently large to be well-representative of the environment in terms of both spatial coverage and temporal coverage. Unfortunately, the task of collecting these samples can be tedious and labor-intensive because one must label each location that is being surveyed. On the other hand, fingerprints without location information are abundant and can easily be collected and so recent studies have tried to capitalize on these unlabeled fingerprints to improve the training set. The paper investigates how this goal can be achieved via graph regularization based on Total Variation (TV). TV is highly effective for semi-supervised learning in image processing but it is not clear whether its success can be transferred to indoor location fingerprinting.
- P. Bahl and V. N. Padmanabhan. Radar: An in-building rf-based user location and tracking system. In INFOCOM, pages 775--784, 2000.Google ScholarCross Ref
- M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res., 7:2399--2434, Dec. 2006. Google ScholarDigital Library
- X. Bresson and R. Zhang. Tv-svm: Total variation support vector machine for semi-supervised data classification. CoRR, abs/1210.0699, 2012.Google Scholar
- M. Brunato and R. Battiti. Statistical learning theory for location fingerprinting in wireless lans. Comput. Netw., 47(6):825--845, Apr. 2005. Google ScholarDigital Library
- O. Chapelle, B. Schlkopf, and A. Zien. Semi-Supervised Learning. The MIT Press, 1st edition, 2010. Google ScholarDigital Library
- Y. Chen, D. Lymberopoulos, J. Liu, and B. Priyantha. Fm-based indoor localization. In Proceedings of the 10th international conference on Mobile systems, applications, and services, MobiSys '12, pages 169--182, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- B. Cheng, J. Yang, S. Yan, Y. Fu, and T. S. Huang. Learning with l1-graph for image analysis. Trans. Img. Proc., 19(4):858--866, Apr. 2010. Google ScholarDigital Library
- A. Elmoataz, O. Lezoray, and S. Bougleux. Nonlocal discrete regularization on weighted graphs: A framework for image and manifold processing. Trans. Img. Proc., 17(7):1047--1060, July 2008. Google ScholarDigital Library
- C. Figuera, J. L. Rojo-Álvarez, M. Wilby, I. Mora-Jiménez, and A. J. Caamano. Advanced support vector machines for 802.11 indoor location. Signal Process., 92(9):2126--2136, Sept. 2012. Google ScholarDigital Library
- Y. jia Zhu, Z. liang Deng, and H. Ji. Indoor localization via l1-graph regularized semi-supervised manifold learning. The Journal of China Universities of Posts and Telecommunications, 19(5):39 -- 91, 2012.Google ScholarCross Ref
- C. Laoudias, D. G. Eliades, P. Kemppi, C. G. Panayiotou, and M. M. Polycarpou. Indoor localization using neural networks with location fingerprints. In Proceedings of the 19th International Conference on Artificial Neural Networks: Part II, ICANN '09, pages 954--963, Berlin, Heidelberg, 2009. Springer-Verlag. Google ScholarDigital Library
- T. Lin, H. Xue, L. Wang, and H. Zha. Total variation and euler's elastica for supervised learning. In ICML, 2012.Google Scholar
- K. Lorincz and M. Welsh. Motetrack: a robust, decentralized approach to rf-based location tracking. Personal Ubiquitous Comput., 11(6):489--503, Aug. 2007. Google ScholarDigital Library
- J. J. Pan and Q. Yang. Co-localization from labeled and unlabeled data using graph laplacian. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, pages 2166--2171, Hyderabad, India, 2007. Google ScholarDigital Library
- J. J. Pan, Q. Yang, H. Chang, and D. Y. Yeung. A manifold regularization approach to calibration reduction for sensor-network based tracking. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pages 988--993, Boston, United States, 2006. Google ScholarDigital Library
- T. Pulkkinen, T. Roos, and P. Myllymaki. Semi-supervised learning for wlan positioning. In Proceedings of the 21th international conference on Artificial neural networks - Volume Part I, ICANN'11, pages 355--362, Berlin, Heidelberg, 2011. Springer-Verlag. Google ScholarDigital Library
- T. Roos, P. Myllymaki, H. Tirri, P. Misikangas, and J. Siev\"anen. A probabilistic approach to WLAN user location estimation. International Journal of Wireless Information Networks, 9(3):155--164, July 2002.Google ScholarCross Ref
- L. I. Rudin, S. Osher, and E. Fatemi. Nonlinear total variation based noise removal algorithms. Phys. D, 60(1--4):259--268, Nov. 1992. Google ScholarDigital Library
- J. B. Tenenbaum, V. D. Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 2000.Google Scholar
- A. Varshavsky, E. de Lara, J. Hightower, A. LaMarca, and V. Otsason. Gsm indoor localization. Pervasive Mob. Comput., 3(6):698--720, Dec. 2007. Google ScholarDigital Library
- K. Whitehouse, C. Karlof, and D. Culler. A practical evaluation of radio signal strength for ranging-based localization. SIGMOBILE Mob. Comput. Commun. Rev., 11(1):41--52, Jan. 2007. Google ScholarDigital Library
- C.-L. Wu, L.-C. Fu, and F.-L. Lian. WLAN location determination in e-home via support vector classification. In Networking, Sensing and Control, 2004 IEEE International Conference on, volume 2, pages 1026--1031 Vol.2, 2004.Google Scholar
- Z. Yang, C. Wu, and Y. Liu. Locating in fingerprint space: wireless indoor localization with little human intervention. In Proceedings of the 18th annual international conference on Mobile computing and networking, Mobicom '12, pages 269--280, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- M. Youssef, A. Agrawala, and A. U. Shankar. Wlan location determination via clustering and probability distributions. In In IEEE PerCom 2003, 2003. Google ScholarDigital Library
Index Terms
- Total variation regularization for training of indoor location fingerprints
Recommendations
Research on Indoor Location Algorithm Based on WIFI
ISBDAI '18: Proceedings of the International Symposium on Big Data and Artificial IntelligenceWith the wide application of mobile Internet, location-based service demands are more and more extensive. In the indoor positioning technology, the location fingerprinting method based on WIFI is widely used because of its strong anti-interference ...
ILOS: a data collection tool and open datasets for fingerprint-based indoor localization
DATA '18: Proceedings of the First Workshop on Data Acquisition To AnalysisFingerprint based indoor localization is promising with distinctive signal readings such as Wi-Fi Received Signal Strength (RSS) and magnetic field in indoor environments. However, collecting location fingerprints is a time consuming process. In this ...
Infrared dim target detection based on total variation regularization and principal component pursuit
Robust detection of infrared dim and small target contributes significantly to the infrared systems in many applications. Due to the diversity of background scene and unique characteristic of target, the detection of infrared targets remains a ...
Comments