The Research of Indoor Positioning Based on Double Wireless Access Points

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Abstract:

Aiming at the problem of low accuracy of indoor positioning by singular wireless access point, this paper proposes a method which is based on the result of weighted calculation using the positioning of two wireless access points. In the experiment, the method use two wireless access point to acquire the signal samples respectively in an office, using the signal propagation model and manifold regularization model to study the surroundings. At the positioning phase, the final position can be obtained by weighted calculating the results of the two wireless access point and the nature of triangular midline. The experimental results show that using the proposed method, the average error value is 20% lower than the corresponding version, using singular wireless access point.

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291-295

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April 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Y. Noh et al., CLIPS: Infrastructure-free collaborative indoor positioning scheme for time-critical team operations., Proc. 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom), IEEE, 2013, pp.172-178.

DOI: 10.1109/percom.2013.6526729

Google Scholar

[2] J. Chua Ching, C. Domingo, K. Iglesia, C. Ngo, N. Chua, Mobile Indoor Positioning Using Wi-fi Localization and Image Processing, Theory and Practice of Computation, pp.242-256: Springer Japan, (2013).

DOI: 10.1007/978-4-431-54436-4_19

Google Scholar

[3] C. Koweerawong, K. Wipusitwarakun, and K. Kaemarungsi, Indoor localization improvement via adaptive RSS fingerprinting database., Proc. 2013 International Conference on Information Networking (ICOIN), IEEE, 2013, pp.412-416.

DOI: 10.1109/icoin.2013.6496414

Google Scholar

[4] M. Pelosi et al., The Role of the Propagation Environment in RSS-Based Indoor Positioning Using Mass Market Devices., Proc. 2012 Loughborough Antennas & Propagation Conference, 2012, pp.1-4.

DOI: 10.1109/lapc.2012.6403032

Google Scholar

[5] Yanliang Jin, Yong Xue, and Yong Zhang, Localization of RSSI-Based Indoor WSN Nodes, Journal of Shanghai University (Natural Science Edition), vol. 18, no. 5, 2012, pp.470-474.

Google Scholar

[6] M. Belkin, P. Niyogi, and V. Sindhwani, Manifold regularization: A geometric framework for learning from labeled and unlabeled examples, The Journal of Machine Learning Research, vol. 7, 2006, pp.2399-2434.

Google Scholar

[7] Yong Zhang, and Xiaoli Zhi, Indoor Positioning Algorithm Based on Semi-supervised Learning, Computer Engineering, vol. 36, no. 17, 2010, pp.277-279.

Google Scholar