Parallel Artificial Neural Network Learning Scheme Based on Radio Frequency Fingerprint for Indoor Localization 


Vol. 43,  No. 6, pp. 979-985, Jun.  2018
10.7840/kics.2018.43.6.979


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  Abstract

The positioning technique based on radio wave fingerprint, which is most commonly used in the indoor positioning field, is most affected by the data comparison algorithm. In this paper, we perform radio wave fingerprint positioning through artificial neural network learning, and the proposed method shows higher performance than the existing Euclidean distance comparison based radio fingerprint positioning algorithm. In this paper, we propose a data extension method as well as a learning structure suitable for indoor positioning, and the proposed data extension method can be partially applied to various positioning techniques. Experimental results show that the proposed technique shows higher performance than the conventional Euclidean distance based positioning method.

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  Cite this article

[IEEE Style]

C. Park and Y. Choi, "Parallel Artificial Neural Network Learning Scheme Based on Radio Frequency Fingerprint for Indoor Localization," The Journal of Korean Institute of Communications and Information Sciences, vol. 43, no. 6, pp. 979-985, 2018. DOI: 10.7840/kics.2018.43.6.979.

[ACM Style]

Chan-Uk Park and Yong-Hoon Choi. 2018. Parallel Artificial Neural Network Learning Scheme Based on Radio Frequency Fingerprint for Indoor Localization. The Journal of Korean Institute of Communications and Information Sciences, 43, 6, (2018), 979-985. DOI: 10.7840/kics.2018.43.6.979.

[KICS Style]

Chan-Uk Park and Yong-Hoon Choi, "Parallel Artificial Neural Network Learning Scheme Based on Radio Frequency Fingerprint for Indoor Localization," The Journal of Korean Institute of Communications and Information Sciences, vol. 43, no. 6, pp. 979-985, 6. 2018. (https://doi.org/10.7840/kics.2018.43.6.979)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880