An Improved Algorithm to Generate a Wi-Fi Fingerprint Database for Indoor Positioning

The major problem of Wi-Fi fingerprint-based positioning technology is the signal strength fingerprint database creation and maintenance. The significant temporal variation of received signal strength (RSS) is the main factor responsible for the positioning error. A probabilistic approach can be used, but the RSS distribution is required. The Gaussian distribution or an empirically-derived distribution (histogram) is typically used. However, these distributions are either not always correct or require a large amount of data for each reference point. Double peaks of the RSS distribution have been observed in experiments at some reference points. In this paper a new algorithm based on an improved double-peak Gaussian distribution is proposed. Kurtosis testing is used to decide if this new distribution, or the normal Gaussian distribution, should be applied. Test results show that the proposed algorithm can significantly improve the positioning accuracy, as well as reduce the workload of the off-line data training phase.

[1]  Ashok K. Agrawala,et al.  Horus: a wlan-based indoor location determination system , 2004 .

[2]  Ruizhi Chen,et al.  Using Inquiry-based Bluetooth RSSI Probability Distributions for Indoor Positioning , 2011 .

[3]  Ruizhi Chen,et al.  Inquiry-Based Bluetooth Indoor Positioning via RSSI Probability Distributions , 2010, 2010 Second International Conference on Advances in Satellite and Space Communications.

[4]  Rosen Ivanov,et al.  Indoor navigation system for visually impaired , 2010, CompSysTech '10.

[5]  Jan Dirk Wegner,et al.  Fusion of Building Information and Range Imaging for Autonomous Location Estimation in Indoor Environments , 2013, Sensors.

[6]  H. Hashemi,et al.  The indoor radio propagation channel , 1993, Proc. IEEE.

[7]  C. Rizos,et al.  Method for yielding a database of location fingerprints in WLAN , 2005 .

[8]  Daniel P. Siewiorek,et al.  Determining User Location For Context Aware Computing Through the Use of a Wireless LAN Infrastructure , 2000 .

[9]  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)..

[10]  K. Kaemarungsi,et al.  Distribution of WLAN received signal strength indication for indoor location determination , 2006, 2006 1st International Symposium on Wireless Pervasive Computing.

[11]  Kostas E. Bekris,et al.  Robotics-Based Location Sensing Using Wireless Ethernet , 2002, MobiCom '02.

[12]  C. Rizos,et al.  Probabilistic Algorithm to Support the Fingerprinting Method for CDMA Location , 2005 .

[13]  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..

[14]  Hao Wang,et al.  A wireless LAN-based indoor positioning technology , 2004, IBM J. Res. Dev..

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

[16]  Henry Tirri,et al.  A Probabilistic Approach to WLAN User Location Estimation , 2002, Int. J. Wirel. Inf. Networks.

[17]  Benny Bing,et al.  Wireless local area networks: the new wireless revolution , 2002 .

[18]  Günther Retscher,et al.  Location determination using WiFi fingerprinting versus WiFi trilateration , 2007, J. Locat. Based Serv..

[19]  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).

[20]  C. Rizos,et al.  Open Source GNSS Reference Server for Assisted-Global Navigation Satellite Systems , 2010, Journal of Navigation.