Bayesian Filter-Based Mobile Tracking under Realistic Network Setting 


Vol. 41,  No. 9, pp. 1060-1068, Sep.  2016


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  Abstract

The range-free localization using connectivity information has problems of mobile tracking. This paper proposes two Bayesian filter-based mobile tracking algorithms considering a propagation scenario. Kalman and Markov Chain Monte Carlo (MCMC) particle filters are applied according to linearity of two measurement models. Measurement models of the Kalman and MCMC particle filter-based algorithms respectively are defined as connectivity between mobiles, information fusion of connectivity information and received signal strength (RSS) from neighbors within one-hop. To perform the accurate simulation, we consider a real indoor map of shopping mall and degree of radio irregularity (DOI) model. According to obstacles between mobiles, we assume two types of DOIs. We show the superiority of the proposed algorithm over existing range-free algorithms through MATLAB simulations.

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  Cite this article

[IEEE Style]

H. Kim and S. Kim, "Bayesian Filter-Based Mobile Tracking under Realistic Network Setting," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 9, pp. 1060-1068, 2016. DOI: .

[ACM Style]

Hyowon Kim and Sunwoo Kim. 2016. Bayesian Filter-Based Mobile Tracking under Realistic Network Setting. The Journal of Korean Institute of Communications and Information Sciences, 41, 9, (2016), 1060-1068. DOI: .

[KICS Style]

Hyowon Kim and Sunwoo Kim, "Bayesian Filter-Based Mobile Tracking under Realistic Network Setting," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 9, pp. 1060-1068, 9. 2016.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880