Differential signal strength fingerprinting revisited

The provision of reliable location estimates in WiFi fingerprinting localization is challenging, mainly because users typically carry heterogeneous devices that report Received Signal Strength (RSS) measurements from surrounding Access Points (AP) very differently. This may render the user-carried device incompatible with the fingerprinting system, in case the RSS radiomap was collected with a different device, thus incurring high localization errors. To this end, we introduce a novel differential fingerprinting method that computes the difference between the RSS value of each AP and the mean RSS value across all APs in the original fingerprint. We show that the new fingerprints are robust to device heterogeneity, as opposed to traditional RSS fingerprints. In addition, we derive analytical results and demonstrate with simulations and experimental data that the proposed approach performs considerably better than existing differential fingerprinting solutions, in terms of localization accuracy and computational overhead.

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

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

[3]  Fangfang Dong,et al.  A Calibration-Free Localization Solution for Handling Signal Strength Variance , 2009, MELT.

[4]  Wee-Seng Soh,et al.  Cramer-Rao Bound Analysis of Localization Using Signal Strength Difference as Location Fingerprint , 2010, 2010 Proceedings IEEE INFOCOM.

[5]  Andreas Haeberlen,et al.  Practical robust localization over large-scale 802.11 wireless networks , 2004, MobiCom '04.

[6]  Seth J. Teller,et al.  Implications of device diversity for organic localization , 2011, 2011 Proceedings IEEE INFOCOM.

[7]  Christoforos Panayiotou,et al.  Device self-calibration in location systems using signal strength histograms , 2013, J. Locat. Based Serv..

[8]  Christoforos Panayiotou,et al.  Device signal strength self-calibration using histograms , 2012, 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[9]  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).

[10]  Mikkel Baun Kjærgaard,et al.  Indoor location fingerprinting with heterogeneous clients , 2011, Pervasive Mob. Comput..

[11]  Shih-Hau Fang,et al.  Calibration-Free Approaches for Robust Wi-Fi Positioning against Device Diversity: A Performance Comparison , 2012, 2012 IEEE 75th Vehicular Technology Conference (VTC Spring).

[12]  Demetrios Zeinalipour-Yazti,et al.  Crowdsourced indoor localization for diverse devices through radiomap fusion , 2013, International Conference on Indoor Positioning and Indoor Navigation.