Server based indoor navigation using RSSI and inertial sensor information

In this paper we present a system for indoor navigation based on received signal strength index information of Wireless-LAN access points and relative position estimates. The relative position information is gathered from inertial smartphone sensors using a step detection and an orientation estimate. Our map data is hosted on a server employing a map renderer and a SQL database. The database includes a complete multilevel office building, within which the user can navigate. During navigation, the client retrieves the position estimate from the server, together with the corresponding map tiles to visualize the user's position on the smartphone display.

[1]  Ruzena Bajcsy,et al.  Precise indoor localization using smart phones , 2010, ACM Multimedia.

[2]  Reinhold Häb-Umbach,et al.  A Novel Similarity Measure for Positioning Cellular Phones by a Comparison With a Database of Signal Power Levels , 2007, IEEE Transactions on Vehicular Technology.

[3]  Youngnam Han,et al.  Improved heading estimation for smartphone-based indoor positioning systems , 2012, 2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC).

[4]  Brian D. O. Anderson,et al.  Wireless sensor network localization techniques , 2007, Comput. Networks.

[5]  Thorsten Vaupel,et al.  A Hidden Markov Model for pedestrian navigation , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.

[6]  Fazli Subhan,et al.  Combined K-Nearest Neighbors and Fuzzy Logic Indoor Localization Technique for Wireless Sensor Network , 2012 .

[7]  Reinhold Häb-Umbach,et al.  Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[8]  Michael Mock,et al.  A step counter service for Java-enabled devices using a built-in accelerometer , 2009, CAMS '09.

[9]  Billur Barshan,et al.  Pedestrian dead reckoning employing simultaneous activity recognition cues , 2012 .

[10]  José Luis Rojo-Álvarez,et al.  Advanced support vector machines for 802.11 indoor location , 2012, Signal Process..

[11]  V. Padmanabhan,et al.  Enhancements to the RADAR User Location and Tracking System , 2000 .