Distance-Based Interpolation and Extrapolation Methods for RSS-Based Localization With Indoor Wireless Signals

Wireless local area network (WLAN)-based fingerprinting using received signal strength (RSS) has been considered to be one solution for indoor positioning. However, one widely recognized problem in fingerprinting is the collection and maintenance of a proper fingerprint database. In this paper, we consider having an incomplete fingerprint database with realistic coverage gaps, and we study the performance of several interpolation and extrapolation methods for recovering the missing fingerprint data. For this purpose, we have collected an extensive set of data at frequency bands of 2.4 and 5 GHz from one university building with four floors. The accuracy of the interpolation and extrapolation methods is studied by artificially removing fingerprints from the database using a randomized procedure and by comparing the estimated fingerprints with the original fingerprints. The average RSS estimation error of different interpolation and extrapolation methods is shown for various percentages of missing fingerprints. In addition, a cumulative RSS error distribution is studied to reveal the dispersion of the error statistics, which affect the user positioning accuracy. Here, the user positioning accuracy is defined in terms of horizontal positioning error and floor detection probability. The user positioning accuracy is also compared in four cases, namely when using the original fingerprints, the partial fingerprints, the interpolated fingerprints, and the interpolated and extrapolated fingerprints. It is shown that both the horizontal positioning accuracy and the floor detection probability can be improved with proper interpolation and extrapolation methods. However, it is also illustrated that the best positioning performance is not necessarily achieved with the best average interpolation and extrapolation accuracy, but it is important to avoid certain types of errors in interpolation and extrapolation.

[1]  Elena Simona Lohan,et al.  Modeling Received Signal Strength measurements for cellular network based positioning , 2013, 2013 International Conference on Localization and GNSS (ICL-GNSS).

[2]  Elena Simona Lohan,et al.  Deconvolution-based indoor localization with WLAN signals and unknown access point locations , 2013, 2013 International Conference on Localization and GNSS (ICL-GNSS).

[3]  Janne Riihijärvi,et al.  Reliability of a radio environment Map: Case of spatial interpolation techniques , 2012, 2012 7th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM).

[4]  Jürgen Götze,et al.  RSS-based channel measurements and their influence on localization in RFID applications , 2013, 2013 IEEE International Symposium on Consumer Electronics (ISCE).

[5]  Hiroshi Akima,et al.  A Method of Bivariate Interpolation and Smooth Surface Fitting for Irregularly Distributed Data Points , 1978, TOMS.

[6]  Markku Renfors,et al.  Statistical Path Loss Parameter Estimation and Positioning using RSS Measurements , 2013 .

[7]  Komwut Wipusitwarakun,et al.  Indoor localization improvement via adaptive RSS fingerprinting database , 2013, The International Conference on Information Networking 2013 (ICOIN).

[8]  Akira Fukuda,et al.  Efficient Survey Database Construction Using Location Fingerprinting Interpolation , 2013, 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA).

[9]  Andrew G. Dempster,et al.  Errors in determinstic wireless fingerprinting systems for localisation , 2008, 2008 3rd International Symposium on Wireless Pervasive Computing.

[10]  Yuwei Chen,et al.  Information filter with speed detection for indoor Bluetooth positioning , 2011, 2011 International Conference on Localization and GNSS (ICL-GNSS).

[11]  Valentin Rakovic,et al.  Location estimation of radio transmitters based on spatial interpolation of RSS values , 2012, 2012 7th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM).

[12]  Markku Renfors,et al.  Statistical path loss parameter estimation and positioning using RSS measurements in indoor wireless networks , 2012, 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[13]  Prashant Krishnamurthy,et al.  Modeling of indoor positioning systems based on location fingerprinting , 2004, IEEE INFOCOM 2004.

[14]  Binghao Li,et al.  Accuracy indicator for fingerprinting localization systems , 2012, Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium.

[15]  Peter Brida,et al.  Rank based fingerprinting algorithm for indoor positioning , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.

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

[17]  Markku Renfors,et al.  COMPARISON OF POSITIONING ACCURACY OF GRID AND PATH LOSS-BASED MOBILE POSITIONING METHODS USING RECEIVED SIGNAL STRENGTHS , 2011 .

[18]  Gabriel Robins,et al.  Real-time RFID localization using RSS , 2013, 2013 International Conference on Localization and GNSS (ICL-GNSS).

[19]  Markku Renfors,et al.  The effect of coverage gaps and measurement inaccuracies in fingerprinting based indoor localization , 2014, International Conference on Localization and GNSS 2014 (ICL-GNSS 2014).

[20]  Mark de Berg,et al.  Computational geometry: algorithms and applications , 1997 .

[21]  Qiang Yang,et al.  Reducing the Calibration Effort for Probabilistic Indoor Location Estimation , 2007, IEEE Transactions on Mobile Computing.

[22]  Simo Ali-Löytty,et al.  A comparative survey of WLAN location fingerprinting methods , 2009, 2009 6th Workshop on Positioning, Navigation and Communication.

[23]  Yongwan Park,et al.  Accurate signal strength prediction based positioning for indoor WLAN systems , 2008, 2008 IEEE/ION Position, Location and Navigation Symposium.

[24]  Elena Simona Lohan,et al.  On the fingerprints dynamics in WLAN indoor localization , 2013, 2013 13th International Conference on ITS Telecommunications (ITST).

[25]  D. Shepard.  A two-dimensional interpolation function for irregularly-spaced data , 1968, ACM National Conference.

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

[27]  Prashant Krishnamurthy,et al.  Sixth Annual IEEE International Conference on Pervasive Computing and Communications Location Fingerprint Analyses Toward Efficient Indoor Positioning , 2022 .

[28]  Richard P. Martin,et al.  The limits of localization using signal strength: a comparative study , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[29]  Yong Cheol Kim,et al.  Fingerprint Wi-Fi Radio Map Interpolated by Discontinuity Preserving Smoothing , 2012, ICHIT.

[30]  Angela Song-Ie Noh,et al.  Comparison of the Mechanisms of the Zigbee's Indoor Localization Algorithm , 2008, 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing.

[31]  Lin Ma,et al.  Dynamic Radio Map Construction for WLAN Indoor Location , 2011, 2011 Third International Conference on Intelligent Human-Machine Systems and Cybernetics.

[32]  R. Franke.  A Critical Comparison of Some Methods for Interpolation of Scattered Data , 1979 .

[33]  Mina Maleki,et al.  A RSS-based fingerprinting method for positioning based on historical data , 2010, Proceedings of the 2010 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS '10).