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Wi-Fi fingerprint positioning updated by Pedestrian dead reckoning for mobile phone indoor localization

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Abstract
The widespread deployment of Wi-Fi communication makes it easy to find Wi-Fi access points in the indoor environment, which enables us to use them for Wi-Fi fingerprint positioning. Although much research is devoted to this topic in the literature, the practical implementation of Wi-Fi based localization is hampered by the variations of the received signal strength (RSS) due to e.g. impediments in the channel, decreasing the positioning accuracy. In order to improve this accuracy, we integrate Pedestrian Dead Reckoning (PDR) with Wi-Fi fingerprinting: the movement distance and walking direction, obtained with the PDR algorithm, are combined with the K-Weighted Nearest Node (KWNN) algorithm to assist in selecting reference points (RPs) closer to the actual position. To illustrate and evaluate our algorithm, we collected the RSS values from 8 Wi-Fi access points inside a building to create a fingerprint database. Simulation results showed that, compared to the conventional KWNN algorithm, the positioning algorithm is improved with 17 %, corresponding to an average positioning error of 1.58 m for the proposed algorithm, while an accuracy of 1.91 m was obtained with the KWNN algorithm. The advantage of the proposed algorithm is that not only the existing Wi-Fi infrastructure and fingerprint database can be used without modification, but also that a standard mobile phone is sufficient to implement our algorithm.
Keywords
Wi-Fi fingerprint, Indoor localization, K-Weighted nearest node algorithm, Pedestrian dead reckoning algorithm

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Chicago
Chang, Qiang, Samuel Van de Velde, Weiping Wang, Qun Li, Hongtao Hou, and Heidi Steendam. 2015. “Wi-Fi Fingerprint Positioning Updated by Pedestrian Dead Reckoning for Mobile Phone Indoor Localization.” In Lecture Notes in Electrical Engineering, 342:729–739. Springer.
APA
Chang, Q., Van de Velde, S., Wang, W., Li, Q., Hou, H., & Steendam, H. (2015). Wi-Fi fingerprint positioning updated by Pedestrian dead reckoning for mobile phone indoor localization. Lecture Notes in Electrical Engineering (Vol. 342, pp. 729–739). Presented at the 6th China Satellite Navigation Conference (CSNC), Springer.
Vancouver
1.
Chang Q, Van de Velde S, Wang W, Li Q, Hou H, Steendam H. Wi-Fi fingerprint positioning updated by Pedestrian dead reckoning for mobile phone indoor localization. Lecture Notes in Electrical Engineering. Springer; 2015. p. 729–39.
MLA
Chang, Qiang, Samuel Van de Velde, Weiping Wang, et al. “Wi-Fi Fingerprint Positioning Updated by Pedestrian Dead Reckoning for Mobile Phone Indoor Localization.” Lecture Notes in Electrical Engineering. Vol. 342. Springer, 2015. 729–739. Print.
@inproceedings{6970076,
  abstract     = {The widespread deployment of Wi-Fi communication makes it easy to find Wi-Fi access points in the indoor environment, which enables us to use them for Wi-Fi fingerprint positioning. Although much research is devoted to this topic in the literature, the practical implementation of Wi-Fi based localization is hampered by the variations of the received signal strength (RSS) due to e.g. impediments in the channel,  decreasing the positioning accuracy. In order to improve this accuracy,
we integrate Pedestrian Dead Reckoning (PDR) with Wi-Fi fingerprinting: the movement distance and walking direction, obtained with the PDR algorithm, are combined with the K-Weighted Nearest Node (KWNN) algorithm to assist in selecting reference points (RPs) closer to the actual position. To illustrate and evaluate our algorithm, we collected the RSS values from 8 Wi-Fi access points inside a building to create a fingerprint database. Simulation results showed that, compared to the conventional KWNN algorithm, the positioning algorithm is improved with 17 \%, corresponding to an average positioning error of 1.58 m for the proposed algorithm, while an accuracy of 1.91 m was obtained with the KWNN algorithm. The advantage of the proposed algorithm is that not only the existing Wi-Fi infrastructure and fingerprint database can be used without modification, but also that a standard mobile phone is sufficient to implement our algorithm.},
  author       = {Chang, Qiang and Van de Velde, Samuel and Wang, Weiping and Li, Qun and Hou, Hongtao and Steendam, Heidi},
  booktitle    = {Lecture Notes in Electrical Engineering},
  isbn         = {978-3-662-46632-2},
  issn         = {1876-1100},
  language     = {eng},
  location     = {Xi'an, china},
  pages        = {729--739},
  publisher    = {Springer},
  title        = {Wi-Fi fingerprint positioning updated by Pedestrian dead reckoning for mobile phone indoor localization},
  url          = {http://dx.doi.org/10.1007/978-3-662-46632-2\_63},
  volume       = {342},
  year         = {2015},
}

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