Unsupervised restoration of generalized multisensor hidden markov chains

This work addresses the problem of generalized mul-tisensor Hidden Markov Chain estimation with application to unsupervised restoration. A Hidden Markov Chain is said to be "generalized" when the exact nature of the noise components is not known; we assume however, that each of them belongs to a finite known set of families of distributions. The observed process is a mixture of distributions and the problem of estimating such a "generalized" mixture thus contains a supplementary difficulty: one has to label, for each state and each sensor, the exact nature of the corresponding distribution. In this work we propose a general procedure with application to estimating generalized multisensor Hidden Markov Chains.