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Communication Dans Un Congrès Année : 2009

Bayesian fixed-interval smoothing algorithms in singular state-space systems

Résumé

Fixed-interval Bayesian smoothing in state-space systems has been addressed for a long time. However, as far as the measurement noise is concerned, only two cases have been addressed so far : the regular case, i.e. with positive definite covariance matrix; and the perfect measurement case, i.e. with zero measurement noise. In this paper we address the smoothing problem in the intermediate case where the measurement noise covariance is positive semi definite (p.s.d.) with arbitrary rank. We exploit the singularity of the model in order to transform the original state-space system into a pairwise Markov model (PMC) with reduced state dimension. Finally, the a posteriori Markovianity of the reduced state enables us to propose a family of fixed-interval smoothing algorithms
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Dates et versions

hal-01366530 , version 1 (14-09-2016)

Identifiants

Citer

Boujemaa Ait-El-Fquih, François Desbouvries. Bayesian fixed-interval smoothing algorithms in singular state-space systems. MLSP 2009 : IEEE International workshop on Machine Learning for Signal Processing, Sep 2009, Grenoble, France. pp.1 - 5, ⟨10.1109/MLSP.2009.5306257⟩. ⟨hal-01366530⟩
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