2012
DOI: 10.1186/1687-6180-2012-134
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Dempster–Shafer fusion of multisensor signals in nonstationary Markovian context

Abstract: The latest developments in Markov models' theory and their corresponding computational techniques have opened new rooms for image and signal modeling. In particular, the use of Dempster-Shafer theory of evidence within Markov models has brought some keys to several challenging difficulties that the conventional hidden Markov models cannot handle. These difficulties are concerned mainly with two situations: multisensor data, where the use of the Dempster-Shafer fusion is unworkable; and nonstationary data, due … Show more

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Cited by 16 publications
(12 citation statements)
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“…A hidden Markov model can be considered a generalization of a mixture model where the hidden variables (or latent variables), which control the mixture component to be selected for each observation, are related through a Markov process rather than independent of each other. Recently, hidden Markov models have been generalized to pairwise Markov models and triplet Markov models which allow consideration of more complex data structures [21] [22] and the modeling of non stationary data [23] [24].…”
Section: A Definition Of Hidden Markov Modelmentioning
confidence: 99%
“…A hidden Markov model can be considered a generalization of a mixture model where the hidden variables (or latent variables), which control the mixture component to be selected for each observation, are related through a Markov process rather than independent of each other. Recently, hidden Markov models have been generalized to pairwise Markov models and triplet Markov models which allow consideration of more complex data structures [21] [22] and the modeling of non stationary data [23] [24].…”
Section: A Definition Of Hidden Markov Modelmentioning
confidence: 99%
“…is not necessarily Markovian. This context has already proven very effective in image segmentation for modeling multiple-stationaries (Lanchantin et al, 2011;Boudaren et al, 2012a), in hidden semi-Markov chains (Lapuyade-Lahorgue et Pieczynski, 2011a) or in hidden evidential Markov chains (Pieczynski, 2007 ;Boudaren et al, 2012b ;Ramasso et Denoeux, 2013), but is novel in optimal statistical filtering. Simulations are provided to illustrate the value of the new modeling in the context of non-stationary on-line filtering of time-series.…”
Section: Extended Abstractmentioning
confidence: 99%
“…peut conduire à une amélioration significative des résultats obtenus (Lanchantin et al, 2011). Au plan plus théorique, les différentes interprétations de N U 1 peuvent mener à différentes modélisations comme les semi-Markov cachés (Lapuyade-Lahorgue et ou les chaînes cachées évidentielles (Pieczynski, 2007 ;Boudaren et al, 2012b ;Ramasso et Denoeux, 2013). Notons également l'utilisation récente du processus auxiliaire pour modéliser, dans le cas multivarié, les états à retard existant entre les canaux observés (Le Cam, 2013).…”
Section: Filtrage Rapide Exact Dans Les « Modèles Cachés Conditionnelunclassified
“…Similarly, the Gaussian mixture model (GMM) [22] can also be considered as a particular TMC. Using Dempster-Shafer fusion [23] in Markovian context leads to another class of TMCs with interesting possibilities of integrating different partial pieces of information in conventional HMCs [24], [25], [26], [27], [28], [29]. In addition, different uses of U can be dealt with simultaneously as, for example, in the case of nonstationary hidden semi-Markov model [26].…”
Section: Introductionmentioning
confidence: 99%