Localization Algorithm Mixing Kalman and Particle Filters for Moving Object in Indoor Wi-Fi Environment 


Vol. 42,  No. 8, pp. 1695-1701, Aug.  2017
10.7840/kics.2017.42.8.1695


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  Abstract

The localization algorithm mixing the Kalman filter (KF) and the Particle filter (PF) for moving object in indoor Wi-Fi environment to reduce the localization error due to the wireless environment factors and to consider the characteristics of the mobility patterns is proposed in this paper. In addition, data for four scenarios of the moving and stationary objects is measured, and the performance of the proposed algorithm is compared with each filter using MATLAB. Experimental results show that the proposed algorithm is not only most effective in the compensation effect of error distance when the linear and curved ratio in assigned scenarios is 1:1 or 1:n topology but also it is more adaptive than the conventional filters even in the section where the moving object path is not constant.

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  Cite this article

[IEEE Style]

G. S. Park and D. M. Lee, "Localization Algorithm Mixing Kalman and Particle Filters for Moving Object in Indoor Wi-Fi Environment," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 8, pp. 1695-1701, 2017. DOI: 10.7840/kics.2017.42.8.1695.

[ACM Style]

Gu Sam Park and Dong Myung Lee. 2017. Localization Algorithm Mixing Kalman and Particle Filters for Moving Object in Indoor Wi-Fi Environment. The Journal of Korean Institute of Communications and Information Sciences, 42, 8, (2017), 1695-1701. DOI: 10.7840/kics.2017.42.8.1695.

[KICS Style]

Gu Sam Park and Dong Myung Lee, "Localization Algorithm Mixing Kalman and Particle Filters for Moving Object in Indoor Wi-Fi Environment," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 8, pp. 1695-1701, 8. 2017. (https://doi.org/10.7840/kics.2017.42.8.1695)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880