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http://dx.doi.org/10.7840/kics.2016.41.9.1060

Bayesian Filter-Based Mobile Tracking under Realistic Network Setting  

Kim, Hyowon (Hanyang University Department of Electronics and Computer Engineering)
Kim, Sunwoo (Hanyang University Department of Electronics Engineering)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.41, no.9, 2016 , pp. 1060-1068 More about this Journal
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.
Keywords
mobile model; Bayesian filter; Kalman filter; Monte Carlo Markov Chain (MCMC) particle filter; tracking algorithm; connectivity information; information fusion; received signal strength (RSS); degree of radio irregularity (DOI);
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