Jaccard Analysis and LASSO-Based Feature Selection for Location Fingerprinting with Limited Computational Complexity

  • Caifa ZhouEmail author
  • Andreas Wieser
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


We propose an approach to reduce both computational complexity and data storage requirements for the online positioning stage of a fingerprinting-based indoor positioning system (FIPS) by introducing segmentation of the region of interest (RoI) into sub-regions, sub-region selection using a modified Jaccard index, and feature selection based on randomized least absolute shrinkage and selection operator (LASSO). We implement these steps into a Bayesian framework of position estimation using the maximum a posteriori (MAP) principle. An additional benefit of these steps is that the time for estimating the position, and the required data storage are virtually independent of the size of the RoI and of the total number of available features within the RoI. Thus the proposed steps facilitate application of FIPS to large areas. Results of an experimental analysis using real data collected in an office building using a Nexus 6P smart phone as user device and a total station for providing position ground truth corroborate the expected performance of the proposed approach. The positioning accuracy obtained by only processing 10 automatically identified features instead of all available ones and limiting position estimation to 10 automatically identified sub-regions instead of the entire RoI is equivalent to processing all available data. In the chosen example, 50% of the errors are less than 1.8 m and 90% are less than 5 m. However, the computation time using the automatically identified subset of data is only about 1% of that required for processing the entire data set.



The China Scholarship Council (CSC) financially supports the first author’s doctoral research. Questions and proposals by three anonymous reviewers are acknowledged for contributing to improved quality of the paper.


  1. Bekkali A, Sanson H, Matsumoto M (2007) Rfid indoor positioning based on probabilistic rfid map and kalman filtering. In: 2007 third ieee international conference on wireless and mobile computing, networking and communications, WiMOB 2007. IEEE, pp 21–21Google Scholar
  2. Chen Y, Yang Q, Yin J, Chai X (2006) Power-efficient access-point selection for indoor location estimation. IEEE Trans Knowl Data Eng 18(7):877–888CrossRefGoogle Scholar
  3. Fastrich B, Paterlini S, Winker P (2015) Constructing optimal sparse portfolios using regularization methods. Comput Manag Sci 12(3):417–434CrossRefGoogle Scholar
  4. Feng C, Au WSA, Valaee S, Tan Z (2012) Received-signal-strength-based indoor positioning using compressive sensing. IEEE Trans Mobile Comput 11(12):1983–1993CrossRefGoogle Scholar
  5. Gu Y, Zhou C, Wieser A, Zhou Z (2017) Pedestrian positioning using wifi fingerprints and a foot-mounted inertial sensor, vol 1, pp 1–9. arXiv:1704.03346
  6. Hazas M, Hopper A (2006) Broadband ultrasonic location systems for improved indoor positioning. IEEE Trans Mobile Comput 5(5):536–547CrossRefGoogle Scholar
  7. He S, Chan S-HG (2016) Wi-fi fingerprint-based indoor positioning: recent advances and comparisons. IEEE Commun Surv Tutor 18(1):466–490Google Scholar
  8. Ingram S, Harmer D, Quinlan M (2004) Ultrawideband indoor positioning systems and their use in emergencies. In: 2004 Position location and navigation symposium, PLANS 2004. IEEE, pp 706–715Google Scholar
  9. Jani SS, Lamb JM, White BM, Dahlbom M, Robinson CG, Low DA (2015) Assessing margin expansions of internal target volumes in 3d and 4d pet: a phantom study. Ann Nucl Med 29(1):100–109CrossRefGoogle Scholar
  10. Kasprzak S, Komninos A, Barrie P (2013) Feature-based indoor navigation using augmented reality. In: 2013 9th international conference on intelligent environments, pp 100–107Google Scholar
  11. Kushki A, Plataniotis KN, Venetsanopoulos AN (2007) Kernel-based positioning in wireless local area networks. IEEE Trans Mobile Comput 6(6):689–705CrossRefGoogle Scholar
  12. Kushki A, Plataniotis KN, Venetsanopoulos AN (2010) Intelligent dynamic radio tracking in indoor wireless local area networks. IEEE Trans Mobile Comput 9(3):405–419CrossRefGoogle Scholar
  13. Lee C, Chang Y, Park G, Ryu J, Jeong S.-G, Park S, Park JW, Lee, HC, Shik Hong K, Lee, MH (2004). Indoor positioning system based on incident angles of infrared emitters. In: 2004 30th annual conference of IEEE industrial electronics society, IECON 2004, pp 2218–2222, vol 3Google Scholar
  14. Madigan D, Einahrawy E, Martin, R. P., Ju, W. H., Krishnan, P., and Krishnakumar, A. S. (2005). Bayesian indoor positioning systems. In: Proceedings IEEE 24th annual joint conference of the ieee computer and communications societie, vol 2, pp 1217–1227Google Scholar
  15. Meinshausen N, Bühlmann P (2010) Stability selection. J R Stat Soc Ser B (Stat Methodol) 72(4):417–473CrossRefGoogle Scholar
  16. Montoliu R, Sansano E, Torres-Sospedra J, Belmonte O (2017) Indoorloc platform: A public repository for comparing and evaluating indoor positioning systems. In: 2017 8th international conference on indoor positioning and indoor navigation, IPIN 2017. IEEE, pp 1–8Google Scholar
  17. Niedermayr S, Wieser A, Neuner H (2014) Expressing location uncertainty in combined feature-based and geometric positioning. In: Proceedings European navigation conference 2014, EUGIN, pp 154–166Google Scholar
  18. Padmanabhan VN, Bahl P (2000) RADAR: an in-building RF based user location and tracking system. In: Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064), vol 2(c), pp 775–784Google Scholar
  19. Park JG, Charrow B, Curtis D, Battat J, Minkov E, Hicks J, Teller S, Ledlie J (2010) Growing an organic indoor location system. In: Proceedings of the 8th international conference on Mobile systems, applications, and services. ACM, pp 271–284Google Scholar
  20. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830Google Scholar
  21. Radu V, Marina MK (2013) Himloc: indoor smartphone localization via activity aware pedestrian dead reckoning with selective crowdsourced wifi fingerprinting. In: International conference on indoor positioning and indoor navigation, pp 1–10Google Scholar
  22. Scott DW (2015) Multivariate density estimation: theory, practice, and visualization. WileyGoogle Scholar
  23. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Series B (Methodol), 267–288Google Scholar
  24. Wang S, Nan B, Rosset S, Zhu J (2011) Random lasso. Ann Appl Stat 5(1):468–485CrossRefGoogle Scholar
  25. Watson DF, Philip GM (1984) Triangle based interpolation. J Int Assoc Math Geol 16(8):779–795CrossRefGoogle Scholar
  26. Youssef M, Agrawala A (2008) The Horus location determination system. Wirel Netw 14(3):357–374Google Scholar
  27. Youssef MA, Agrawala A, Shankar AU (2003) Wlan location determination via clustering and probability distributions. In: 2003 Proceedings of the First IEEE International Conference on Pervasive computing and communications, (PerCom 2003). IEEE, pp 143–150Google Scholar
  28. Zhang T (2011) Adaptive forward-backward greedy algorithm for learning sparse representations. IEEE Trans Inf Theory 57(7):4689–4708CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Institute of Geodesy & PhotogrammetryETH ZürichZürichSwitzerland

Personalised recommendations