A COMPARATIVE STUDY OF ADAPTIVE MCMC BASED PARTICLE FILTERING METHODS
Jae Yoon · 2012
In this thesis, we present a comparative study of conventional particle filtering (PF) algorithms for tracking applications. Through the review from the generic PF to more recent Markov chain Monte Carlo (MCMC) based PFs, we will revisit the sample impoverishment problem. For all PF methods using resampling process, maintaining appropriate sample diversity is a big problem. Although Gilks et al. proposed an MCMC based PF to avoid this problem, their method sometimes fails due to small process noise. Therefore, we propose an improved MCMC move PF method which employs an adaptive MCMC move. This adaptive MCMC process elastically manages the MCMC proposal density function to circumvent the sample impoverishment problem efficiently and gives better sample diversity for posterior approximation. ( en )