Uncertainty modelling and computational aspects of data association

Abstract A novel solution to the smoothing problem for multi-object dynamical systems is proposed and evaluated. The of interest contain an unknown varying number objects that are partially observed under noisy corrupted observations. In order account lack information about different aspects this type complex system, alternative representation uncertainty based on possibility theory considered. It shown how analogues usual concepts such as Markov chains hidden models (HMMs) can be introduced in context. particular, considered statistical model multiple formulated a hierarchical consisting conditionally independent HMMs. This structure leveraged propose efficient method context chain Monte Carlo (MCMC) by relying approximate corresponding filtering problem, similar fashion particle MCMC. approach outperform existing algorithms range scenarios.