A New Three-Dimensional Indoor Positioning Mechanism Based on Wireless LAN

The researches on two-dimensional indoor positioning based on wireless LAN and the location fingerprint methods have become mature, but in the actual indoor positioning situation, users are also concerned about the height where they stand. Due to the expansion of the range of three-dimensional indoor positioning, more features must be needed to describe the location fingerprint. Directly using a machine learning algorithm will result in the reduced ability of classification. To solve this problem, in this paper, a “divide and conquer” strategy is adopted; that is, first through k-medoids algorithm the three-dimensional location space is clustered into a number of service areas, and then a multicategory SVM with less features is created for each service area for further positioning. Our experiment shows that the error distance resolution of the approach with k-medoids algorithm and multicategory SVM is higher than that of the approach only with SVM, and the former can effectively decrease the “crazy prediction.”

[1]  Xu Lian-ming.  A K-Means Based Method to Identify Floor in WLAN Indoor Positioning System , 2012 .

[2]  J. Werb,et al.  Designing a positioning system for finding things and people indoors , 1998 .

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[4]  Uwe Grossmann,et al.  THE ACCURACY OF ALGORITHMS FOR WLAN INDOOR POSITIONING AND THE STANDARDIZATION OF SIGNAL RECEPTION FOR DIFFERENT MOBILE DEVICES , 2014 .

[5]  Gordon L. Stüber,et al.  Overview of radiolocation in CDMA cellular systems , 1998, IEEE Commun. Mag..

[6]  Per K. Enge,et al.  Global positioning system: signals, measurements, and performance [Book Review] , 2002, IEEE Aerospace and Electronic Systems Magazine.

[7]  Roberto Battiti,et al.  Location-aware computing: a neural network model for determining location in wireless LANs , 2002 .

[8]  Fuwen Yang,et al.  Observer-based H ∞ control for discrete-time stochastic systems with quantisation and random communication delays , 2013 .

[9]  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).

[10]  Peter J. Rousseeuw,et al.  Clustering by means of medoids , 1987 .

[11]  Yunhao Liu,et al.  LANDMARC: Indoor Location Sensing Using Active RFID , 2004, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[12]  Hao Zhang,et al.  Quantized Control Design for Impulsive Fuzzy Networked Systems , 2011, IEEE Transactions on Fuzzy Systems.

[13]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

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

[15]  Domenico Cotroneo,et al.  Implementing positioning services over an ubiquitous infrastructure , 2004 .

[16]  Fuwen Yang,et al.  Distributed average filtering for sensor networks with sensor saturation , 2013 .

[17]  Fuwen Yang,et al.  Quantized H∞ Control for Networked Systems with Communication Constraints , 2013 .

[18]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[19]  Jing Liu,et al.  Survey of Wireless Indoor Positioning Techniques and Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

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

[21]  J. Barney,et al.  Commercialization of an ultra wideband precision asset location system , 2003, IEEE Conference on Ultra Wideband Systems and Technologies, 2003.

[22]  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)..

[23]  S. Gansemer,et al.  3D WLAN indoor positioning in multi-storey buildings , 2009, 2009 IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications.

[24]  Zong-xin Wang,et al.  A New WLAN Indoor Localization System Based on Distance-loss Model with Area Partition: A New WLAN Indoor Localization System Based on Distance-loss Model with Area Partition , 2011 .