Refining WI-FI Based Indoor Positioning

The increasing demand for location-based services inside buildings has made indoor positioning a significant research topic. This study deals with indoor positioning using the Wireless Ethernet IEEE 802.11 (Wi-Fi) standard that has a distinct advantage of low cost over other indoor wireless technologies. Most of the proposed Wi-Fi indoor positioning systems use either proximity detection via radio signal propagation models or location fingerprinting techniques, the latter being usually more accurate. The aim of this study is to examine several aspects of Wi-Fi location fingerprinting based indoor positioning that could enhance the positioning accuracy, without demanding a larger radio map with additional signal strength measurements in more locations, namely making use of weakly-sensed access points, making use of the different available Wi-Fi frequency bands, using device’s orientation information provided by a built-in digital compass, and augmenting the radio map using Locally Weighted Regression.

[1]  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).

[2]  M. Manic,et al.  Wireless based object tracking based on neural networks , 2008, 2008 3rd IEEE Conference on Industrial Electronics and Applications.

[3]  R. Battiti,et al.  Neural network models for intelligent networks : deriving the location from signal patterns , 2002 .

[4]  Tsung-Nan Lin,et al.  Performance comparison of indoor positioning techniques based on location fingerprinting in wireless networks , 2005, 2005 International Conference on Wireless Networks, Communications and Mobile Computing.

[5]  Andrew G. Dempster,et al.  Indoor Positioning Techniques Based on Wireless LAN , 2007 .

[6]  Federico Thomas,et al.  Revisiting trilateration for robot localization , 2005, IEEE Transactions on Robotics.

[7]  Prashant Krishnamurthy,et al.  Properties of indoor received signal strength for WLAN location fingerprinting , 2004, The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004..

[8]  O.M. Badawy,et al.  Decision Tree Approach to Estimate User Location in WLAN Based on Location Fingerprinting , 2007, 2007 National Radio Science Conference.

[9]  Martin Klepal,et al.  Influence of Predicted and Measured Fingerprint on the Accuracy of RSSI-based Indoor Location Systems , 2007, 2007 4th Workshop on Positioning, Navigation and Communication.

[10]  W. Cleveland,et al.  Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting , 1988 .

[11]  Andrew W. Moore,et al.  Locally Weighted Learning , 1997, Artificial Intelligence Review.

[12]  I-En Liao,et al.  Enhancing the accuracy of WLAN-based location determination systems using predicted orientation information , 2008, Inf. Sci..

[13]  A. S. Krishnakumar,et al.  Bayesian indoor positioning systems , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[14]  Jaegeol Yim,et al.  Improvement of Kalman filters for WLAN based indoor tracking , 2010, Expert Syst. Appl..

[15]  Dieter Fox,et al.  Gaussian Processes for Signal Strength-Based Location Estimation , 2006, Robotics: Science and Systems.

[16]  Hien Nguyen Van,et al.  Indoor Localization Using Multiple Wireless Technologies , 2007, 2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems.

[17]  Jaegeol Yim,et al.  Introducing a decision tree-based indoor positioning technique , 2008, Expert Syst. Appl..

[18]  Simo Ali-Löytty,et al.  A comparative survey of WLAN location fingerprinting methods , 2009, 2009 6th Workshop on Positioning, Navigation and Communication.

[19]  M. Klepal,et al.  Effective indoor propagation predictions , 2001, IEEE 54th Vehicular Technology Conference. VTC Fall 2001. Proceedings (Cat. No.01CH37211).

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