A robust location fingerprint based on differential signal strength and dynamic linear interpolation

Received signal strength RSS in wireless networks is widely adopted for indoor positioning purpose because of its low cost and open access properties. However, the popular RSSs are observed to differ significantly from discrete devices' hardware even under the same wireless conditions. Signal strength difference-based approach is an efficient strategy to overcome the drawbacks of RSSs. In this paper, we present a robust location fingerprint based on SSD. Firstly, a beacon selection algorithm is proposed to choose the beacons that have the best distinguishing results and stability as reference beacons. Secondly, a robust location fingerprint based on differential signal strength and dynamic linear interpolation is suggested to reduce the high labor cost in offline phase. The evaluation results show that the proposed approaches can achieve higher accuracy and robustness. Copyright © 2016 John Wiley & Sons, Ltd.

[1]  Jieh-Chian Wu,et al.  Analysis of hyperbolic and circular positioning algorithms using stationary signal-strength-difference measurements in wireless communications , 2006, IEEE Transactions on Vehicular Technology.

[2]  Hien Nguyen Van,et al.  SSD: A Robust RF Location Fingerprint Addressing Mobile Devices' Heterogeneity , 2013, IEEE Transactions on Mobile Computing.

[3]  Hao-Hua Chu,et al.  Unsupervised Learning for Solving RSS Hardware Variance Problem in WiFi Localization , 2009, Mob. Networks Appl..

[4]  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).

[5]  Dario Petri,et al.  Accuracy of RSS-Based Centroid Localization Algorithms in an Indoor Environment , 2011, IEEE Transactions on Instrumentation and Measurement.

[6]  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)..

[7]  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).

[8]  Mikkel Baun Kjærgaard,et al.  Hyperbolic Location Fingerprinting: A Calibration-Free Solution for Handling Differences in Signal Strength (concise contribution) , 2008, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom).

[9]  Kung Yao,et al.  New wireless acoustic array node for localization, beamforming and source separation for bio-complexity bird data collection and study , 2013, 2013 IEEE China Summit and International Conference on Signal and Information Processing.

[10]  Konstantinos N. Plataniotis,et al.  Intelligent Dynamic Radio Tracking in Indoor Wireless Local Area Networks , 2010, IEEE Transactions on Mobile Computing.

[11]  Xiaoliang Yang,et al.  An Improved RFID-Based Localization Algorithm for Wireless Sensor Networks , 2013, Int. J. Distributed Sens. Networks.

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

[13]  Yunhao Liu,et al.  VIRE: Active RFID-based Localization Using Virtual Reference Elimination , 2007, 2007 International Conference on Parallel Processing (ICPP 2007).

[14]  Ana M. Bernardos,et al.  Real time calibration for RSS indoor positioning systems , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[15]  Shih-Hau Fang,et al.  A Novel Algorithm for Multipath Fingerprinting in Indoor WLAN Environments , 2008, IEEE Transactions on Wireless Communications.