Abstract
Unlike the conventional particle filters, particle flow filters do not rely on proposal density and importance sampling; they employ flow of the particles through a methodology derived from the log-homotopy scheme and ensure successful migration of the particles. Amongst the efficient implementations of particle filters, Exact Daum-Huang (EDH) filter pursues the calculation of migration parameters all together. An improved version of it, Localized Exact Daum-Huang (LEDH) filter, calculates the migration parameters separately. In this study, the main objective is to reduce the cost of calculation in LEDH filters which is due to exhaustive calculation of each migration parameter. We proposed the Clustered Exact Daum-Huang (CEDH) filter. The main impact of CEDH is the clustering of the particles considering the ones producing similar errors and then calculating the same migration parameters for the particles within each cluster. Through clustering and handling the particles with high errors, their engagement and influence can be balanced, and the system can greatly reduce the negative effects of such particles on the overall system. We implement the filter successfully for the scenario of high dimensional target tracking. The results are compared to those obtained with EDH and LEDH filters to validate its efficiency.
Highlights
For the analysis, inference, and comprehensive understanding of a dynamic system, two models are essentially required
We proposed the Clustered Exact Daum-Huang (CEDH) filter
CEDH filters fight to reduce the cost of calculation in Localized Exact Daum-Huang (LEDH) filters which typically occurs due to exhaustive calculation of each migration parameter
Summary
Inference, and comprehensive understanding of a dynamic system, two models are essentially required. Daum et al [26] extended their studies through derivation of a new exact stochastic particle flow filter using a theorem established by Gromov They conducted numerical experiments for a number of different high dimensional problems. In [27], the researchers combined the strengths of invertible particle flow and sequential Markov chain Monte Carlo (SMCMC) through constructing a composite Metropolis-Hastings (MH) kernel They proposed a Gaussian mixture model- (GMM-) based particle flow algorithm to construct effective MH kernels. This will provide reduced cost of calculation while maintaining the performance of LEDH. Performance comparisons of the filters employed for multitarget tracking problem in the high dimensional state space are given as well.
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