Indoor Localization Using Improved RSS-Based Lateration Methods

Location estimation is a critical step for many location-aware applications. To obtain location information, localization methods employing Received Signal Strength (RSS) are attestative since it can reuse the existing wireless infrastructure for localization. Among the large class of localization schemes, RSS-based lateration methods have the advantage of providing closed-form solutions for mathematical analysis as compared to heuristic-based localization approaches. However, the localization accuracy of RSS-based lateration methods are significantly affected by the unpredictable setup in indoor environments. To improve the applicability of RSS-based lateration methods in indoors, we propose two approaches, regression-based and correlation-based. The regression-based approach uses linear regression to discover a better fit of signal propagation model between RSS and the distance, while the correlation-based approach utilizes the correlation among RSS in local area to obtain more accurate signal propagation. Our results using both simulation as well as real experiments demonstrate that our improved methods outperform the original RSS-based lateration methods significantly.

[1]  Jie Yang,et al.  A theoretical analysis of wireless localization using RF-based fingerprint matching , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[2]  Yingying Chen,et al.  Robust wireless localization to attacks on access points , 2009, 2009 IEEE Sarnoff Symposium.

[3]  R.L. Moses,et al.  Locating the nodes: cooperative localization in wireless sensor networks , 2005, IEEE Signal Processing Magazine.

[4]  Richard P. Martin,et al.  The Impact of Using Multiple Antennas on Wireless Localization , 2008, 2008 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[5]  Larry J. Greenstein,et al.  An empirical indoor path loss model for ultra-wideband channels , 2003, Journal of Communications and Networks.

[6]  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).

[7]  Ainslie,et al.  CORRELATION MODEL FOR SHADOW FADING IN MOBILE RADIO SYSTEMS , 2004 .

[8]  Reiner S. Thomä,et al.  Correlation Properties of Large Scale Fading Based on Indoor Measurements , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[9]  P. Turner,et al.  Numerical methods and analysis , 1992 .

[10]  X. Jia,et al.  An indoor wireless positioning system based on wireless local area network infrastructure , 2003 .

[11]  Eyal de Lara,et al.  Calibree: Calibration-Free Localization Using Relative Distance Estimations , 2009, Pervasive.

[12]  Richard P. Martin,et al.  A Practical Approach to Landmark Deployment for Indoor Localization , 2006, 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks.

[13]  Guenther Retscher,et al.  Integration of RFID, GNSS and DR for Ubiquitous Positioning in Pedestrian Navigation , 2007 .

[14]  B. R. Badrinath,et al.  Ad hoc positioning system (APS) , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[15]  Richard P. Martin,et al.  Attack Detection in Wireless Localization , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[16]  Koen Langendoen,et al.  Distributed localization in wireless sensor networks: a quantitative compariso , 2003, Comput. Networks.