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Indoor WiFi Positioning

Chapter
Part of the Navigation: Science and Technology book series (NASTECH)

Abstract

This chapter studies WiFi based wireless positioning in complex indoor environments. WiFi positioning makes use of the infrastructure (WiFi access points) widely deployed in indoor environments such as office buildings, teaching buildings, hospitals, and shopping centers. It is also a fact that WiFi technology has been adopted in billions of electronic devices such as smartphones. Although WiFi positioning is cost-effective, it suffers the drawback of low positioning accuracy and hence innovative techniques are required to enhance WiFi positioning accuracy.

Keywords

WiFi Positioning Complex Indoor Environment Access Point (APs) Smoothness Index WKNN Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Kegen Yu would like to thank two Ph.D. students, Weixing Xue and Wei Zhang, for providing the experimental results, and Professor Xianghong Hua for useful discussions.

References

  1. Borel CC (1998) Surface emissivity and temperature retrieval for a hyperspectral sensor. In: Proceedings of IEEE international geoscience and remote sensing symposium, Seattle, WA, USA, pp 546–549Google Scholar
  2. Chao CH, Chu CY, Wu AY (2008) Location-constrained particle filter for RSSI-based indoor human positioning and tracking system. In: Proceedings of IEEE workshop on signal processing systems, Washington, D.C., USA, pp 73–76Google Scholar
  3. 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
  4. Deng Z, Ma L, Xu Y (2011) Intelligent AP selection for indoor positioning in wireless local area network. In: Proceedings of international ICST conference on communications and networking in China, Harbin, China, pp 257–261Google Scholar
  5. Hua X, Zhang W, Yu K, Qiu W, Zhang S, Chang X (2017) Performance analysis for AP selection strategy. In: Proceedings of China satellite navigation conference, Shanghai, China, pp 325–333CrossRefGoogle Scholar
  6. Jekabsons G, Kairish V, Zuravlyov V (2011) An analysis of Wi-Fi based indoor positioning accuracy. Sci J Riga Tech Univ 47:131–137Google Scholar
  7. Mansour MF (2014) Kalman filter for indoor positioning. Patent US20140368386Google Scholar
  8. Rappaport TS (2002) Wireless communications principles and practices. Prentice-HallGoogle Scholar
  9. Wang X, Song SZ, Li M (2012) Design of personnel position system of mine based on the average of RSSI. In: Proceedings of IEEE international conference on automation & logistics, Zhengzhou, China, pp 239–242Google Scholar
  10. Xue W, Qiu W, Hua X, Yu K (2017) Improved Wi-Fi RSSI measurement for Indoor localization. IEEE Sens J 17(7):2224–2230CrossRefGoogle Scholar
  11. Youssef M, Agrawala A, Shankar AU (2003) WLAN location determination via clustering and probability distributions. In: Proceedings of IEEE international conference on pervasive computing and communications, Fort Worth, Texas, USA, pp 143–150Google Scholar
  12. Zhang W, Hua X, Yu K, Qiu W, Chang X, Chen X (2017) Radius based domain clustering for WiFi indoor positioning. Sens Rev 37(1):54–60CrossRefGoogle Scholar
  13. Zou H, Luo Y, Lu X, Jiang H, Xie L (2015) A mutual information based online access point selection strategy for WiFi indoor localization. In: Proceedings of IEEE international conference on automation science and engineering (CASE), Gothenburg, Sweden, pp 180–185Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.CSIRO ICT CentreMarsfieldAustralia
  2. 2.China University of Mining & TechnologyXuzhouChina

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