Advertisement

Weight Matrix Analysis Algorithm for WLAN Indoor Positioning System

  • Lin Ma
  • Jian Li
  • Yubin Xu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 251)

Abstract

Because WLAN signal strength data is vulnerable to external interference and its validity period is short-lived, it is necessary to reconstruct radio map to improve positioning performance. We add the weight data to the original fingerprint library, which is obtained by the reliability of information. Using it, we can know the importance of neighborhood points selected in the online phase and get better positioning performance. In the positioning phase, the KD tree is added to improve the positioning efficiency of the positioning algorithm. Finally, the positioning accuracy and efficiency can be improved.

Keywords

Indoor positioning WLAN Radio map Weight matrix analysis 

Notes

Acknowledgment

This paper is supported by National Natural Science Foundation of China (61571162), Ministry of Education - China Mobile Research Foundation (MCM20170106) and Heilongjiang Province Natural Science Foundation (F2016019).

References

  1. 1.
    Gartner, G., Ortag, F.: Advances in location-based services. Lect. Notes Geoinformation Cartogr. 5(8), 97–106 (2014)Google Scholar
  2. 2.
    Feng, C., Au, W.S.A., Valaee, S., et al.: Received-signal-strength-based indoor positioning using compressive sensing. IEEE Trans. Mob. Comput. 11(12), 1983–1993 (2012)CrossRefGoogle Scholar
  3. 3.
    Baala, O., Zheng, Y., Caminada, A.: The impact of AP placement in WLAN-based indoor positioning system. In: Eighth International Conference on Networks, pp. 12–17. IEEE Computer Society (2009)Google Scholar
  4. 4.
    Pan, J.J., Pan, S.J., Yin, J., et al.: Tracking mobile users in wireless networks via semi-supervised colocalization. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 587–600 (2012)CrossRefGoogle Scholar
  5. 5.
    Au, A.W.S., Feng, C., Valaee, S., et al.: Indoor tracking and navigation using received signal strength and compressive sensing on a mobile device. IEEE Trans. Mob. Comput. 12(10), 2050–2062 (2013)CrossRefGoogle Scholar
  6. 6.
    Bong, W., Kim, Y.C.: Reconstruction of radio map from sparse RSS data by discontinuity preserving smoothing. In: ACM Research in Applied Computation Symposium, pp. 227–231. ACM (2012)Google Scholar
  7. 7.
    Li, X., Deng, Z.: Radio map reconstruction technology in indoor fingerprint localization algorithm (2012)Google Scholar
  8. 8.
    Deng, Z., Ma, L., Xu, Y.: Intelligent AP selection for indoor positioning in wireless local area network. In: International ICST Conference on Communications and Networking in China, pp. 257–261. IEEE Computer Society (2011)Google Scholar
  9. 9.
    Umair, M.Y., Xiao, D., Li, A., et al.: Access point selection for indoor positioning in a WLAN environment using an algorithm based on RSSI and dilution of precision. Environ. Entomol. 26(3), 91–99 (2014)Google Scholar
  10. 10.
    Yang, L., Chen, H., Cui, Q., et al.: Probabilistic-KNN: a novel algorithm for passive indoor-localization scenario. In: Vehicular Technology Conference, pp. 1–5. IEEE (2015)Google Scholar
  11. 11.
    Chen, X.K., Liu, Z.S.: K nearest neighbor query based on improved Kd-tree construction algorithm. J. Guangdong Univ. Technol. 31, 119–123 (2014)Google Scholar
  12. 12.
    Kaemarungsi, K., Krishnamurthy, P.: Analysis of WLAN’s received signal strength indication for indoor location fingerprinting. Elsevier Science Publishers B. V. (2012)Google Scholar
  13. 13.
    Lee, M., Han, D.: Voronoi tessellation based interpolation method for Wi-Fi radio map construction. IEEE Commun. Lett. 16(3), 404–407 (2012)CrossRefGoogle Scholar
  14. 14.
    Abubaker, M., Ashour, W.: Efficient data clustering algorithms: improvements over kmeans. Int. J. Intell. Syst. Appl. 5(3), 37–49 (2013)Google Scholar
  15. 15.
    Wang, L., Wong, W.C.: A RSS based statistical localization algorithm in WLAN. In: International Conference on Signal Processing and Communication Systems, pp. 1–5 (2012)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.Communication Research CenterHarbin Institute of TechnologyHarbinChina

Personalised recommendations