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RSSI-Based Fingerprint Positioning System for Indoor Wireless Network

  • Conference paper
Intelligent Computing in Smart Grid and Electrical Vehicles (ICSEE 2014, LSMS 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 463))

Abstract

This paper presents a direct explicit method of the fingerprint positioning for indoor wireless network. In data collection, for the purpose of a reliable and stable signal, a feedback filter is added to the sampler. In positioning phase, the location clustering technique is used to exclude invalid reference points. Then a matching algorithm based on RSSI correlation coefficient is proposed, which can improve positioning accuracy. The example in the paper illustrates the effectiveness of the proposed positioning scheme.

This work is supported by the National Natural Science Foundation of China (61273026) and the Fundamental Research Funds for the Central Universities.

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References

  1. Wi-Fi Positioning Technology, http://labs.chinamobile.com/mblog/712208_82886

  2. Cheng, X., Thaeler, A., Xue, G., Chen, D.: TPS: A Time-Based Positioning Schemes for Outdoor Wireless Sensor Network. IEEE INFOCOM 4(4), 2685–2696 (2004)

    Google Scholar 

  3. Belloni, F., Ranki, V., Kainulainen, A., Richter, A.: Angle-Based Indoor Positioning System for Open Indoor Environment. In: 2009 6th Workshop on Positioning, Navigation and Communication, pp. 261–265 (2009)

    Google Scholar 

  4. Ren, W., Xu, L., Deng, Z., Wang, C.: Positioning Algorithm Using Maximum Likelihood Estimation of RSSI Difference in Wireless Sensor Networks. Journal of Data Acquisition & Processing 21(7), 1247–1250 (2008)

    Google Scholar 

  5. Zhang, X., Zhao, P., Xu, G., Lin, R.: Research of Indoor Positioning Based on A Optimization KNN Algorithm. International Electronic Elements 21(7), 44–46 (2013)

    Google Scholar 

  6. Sakamoto, J., Miura, H., Matsuda, N., Taki, H., Abe, N., Hori, S.: Indoor Location Determination Using a Topological Model. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3684, pp. 143–149. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  7. Brunato, M., Battiti, R.: Statistical Learning Theory for Location Fingerprinting in Wireless LANs. Computer Networks 47(6), 825–845 (2005)

    Article  MATH  Google Scholar 

  8. Fang, S., Lin, T., Lin, P.: Location Fingerprinting In A Decorrelated Space. IEEE Transactions on Knowledge and Data Engineering 20(5), 685–691 (2008)

    Article  Google Scholar 

  9. Milioris, D., Tzagkarakis, G., Papakonstantinou, A., Papadopouli, M., Tsakalides, P.: Low-Dimensional Signal-Strength Fingerprint-Based Positioning in Wireless. Lans Ad Hoc Networks 12, 100–114 (2014)

    Article  Google Scholar 

  10. Liang, X., Gou, X., Liu, Y.: Fingerprint-Based Location Positioning Using Improved KNN. In: 2012 3rd IEEE International Conference on Network Infrastructure and Digital Content, pp. 57–61 (2012)

    Google Scholar 

  11. Peerapong, T., Xiu, C.: Indoor Positioning Based on Wi-Fi Fingerprint Technique Using Fuzzy K-Nearest Neighbor. In: Proceedings of 2014 11th International Bhurban Conference on Applied Sciences & Technology, pp. 461–465 (2014)

    Google Scholar 

  12. Ni, L.M., Liu, Y., Lau, Y.C., Patil, A.P.: LANDMARC: Indoor Location Sensing Using Active RFID. Wireless Networks 10(6), 701–710 (2004)

    Article  Google Scholar 

  13. Tian, F., Dong, Y., Sun, E., Wang, C.: Nodes Localization Algorithm for Linear Wireless Sensor Networks in Underground Coal Mine Based on RSSI-Similarity Degree. In: 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1–4 (2011)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Control Science and Engineering, Tongji University, Shanghai, 200092, P.R.China

    Ruohan Yang & Hao Zhang

Authors
  1. Ruohan Yang
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  2. Hao Zhang
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Editor information

Editors and Affiliations

  1. The Queen’s University, Belfast, UK

    Kang Li

  2. State Grid Electric Power Research Institute, Jiangsu, China

    Yusheng Xue

  3. Harbin Institute of Technology, China

    Shumei Cui

  4. Shanghai University, China

    Qun Niu

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© 2014 Springer-Verlag Berlin Heidelberg

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Yang, R., Zhang, H. (2014). RSSI-Based Fingerprint Positioning System for Indoor Wireless Network. In: Li, K., Xue, Y., Cui, S., Niu, Q. (eds) Intelligent Computing in Smart Grid and Electrical Vehicles. ICSEE LSMS 2014 2014. Communications in Computer and Information Science, vol 463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45286-8_33

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  • DOI: https://doi.org/10.1007/978-3-662-45286-8_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45285-1

  • Online ISBN: 978-3-662-45286-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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