An Indoor Localization of WiFi Based on Support Vector Machines

Feng Yu; Ming Hua Jiang; Jing Liang; Xiao Qin; Ming Hu; Tao Peng; Xin Rong Hu

doi:10.4028/www.scientific.net/AMR.926-930.2438

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 926-930An Indoor Localization of WiFi Based on Support...

An Indoor Localization of WiFi Based on Support Vector Machines

Abstract:

The recent growing interest for indoor localization-based services has created a need for more accurate and real-time indoor localization solutions. Indoor localization based on existing WiFi signal strength is becoming increasingly prevalent and ubiquity. In this paper, we utilize the information of the signal strength received from the surrounding access points (APs) to determine the user localization. The propose algorithm based on support vector machines (SVM) algorithm, and comparing with three kernel functions, radial basis function (RBF) performs best of all. Experimental results indicate that the proposed algorithm leads to improvement on localization accuracy.

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Periodical:

Advanced Materials Research (Volumes 926-930)

Pages:

2438-2441

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.926-930.2438

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Online since:

May 2014

Authors:

Feng Yu*, Ming Hua Jiang, Jing Liang, Xiao Qin, Ming Hu, Tao Peng, Xin Rong Hu

Keywords:

Fingerprint Map, Indoor Localization, Radial Basis Function (RBF), Support Vector Machine (SVM), WIFI

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References

[1] Y. Kim, Y. Chon, and H. Cha: IEEE Transactions on, Vol. 42 (2012), No. 1, pp.112-122.

Google Scholar

[2] V. Moghtadaiee and A. G. Dempster: WiFi fingerprinting signal strength error modeling for short distances, " in Indoor Positioning and Indoor Navigation (IPIN), 2012 International Conference on, pp.1-6, IEEE, (2012).

DOI: 10.1109/ipin.2012.6418852

Google Scholar

[3] J. Yin, Q. Yang, and L. M. Ni: IEEE Transactions on, Vol. 7 (2008), No. 7, pp.869-883.

Google Scholar

[4] T. -y. Chin: Rss-based doa indoor location estimation system and method, Jan. 24 2013. US Patent 20, 130, 023, 278.

Google Scholar

[5] M. Bocquet, C. Loyez, and A. Benlarbi-Delai: Microwave and Wireless Components Letters, IEEE, Vol. 15 (2005), No. 10, pp.612-614.

DOI: 10.1109/lmwc.2005.855392

Google Scholar

[6] C. Wong, R. Klukas, and G. G. Messier, VTC 2008-Fall, IEEE 68th, pp.1-5, IEEE, (2008).

Google Scholar

[7] Y. Luo, O. Hoeber, and Y. Chen, Human-centric Computing and Information Sciences, Vol. 3, No. 1, pp.1-23, (2013).

Google Scholar

[8] C. Sapumohotti, M. Y. Alias, and S. W. Tan: Progress In Electromagnetics Research B, Vol. 46 (2013), pp.1-22.

Google Scholar