Kalman filtering in triplet Markov chains

Boujemaa Ait-El-Fquih; François Desbouvries

doi:10.1109/TSP.2006.877651

Kalman filtering in triplet Markov chains

Boujemaa Ait-El-Fquih
, François Desbouvries

CNRS UMR 5157 SAMOVAR

Résultats de recherche: Contribution à un journal › Article › Revue par des pairs

Résumé

Let x = {X_n}_nεIN be a hidden process, y = {Y_n}_nεIN an observed process, and r = {r_n}_nεIN some additional process. We assume that t = (x r y) is a (so-called "Triplet") vector Markov chain (TMC). We first show that the linear TMC model encompasses and generalizes, among other models, the classical state-space systems with colored process and/or measurement noise(s). We next propose restoration Kalman-like filters for arbitrary linear Gaussian (LG) TMC.

langue originale	Anglais
Pages (de - à)	2957-2963
Nombre de pages	7
journal	IEEE Transactions on Signal Processing
Volume	54
Numéro de publication	8
Les DOIs	https://doi.org/10.1109/TSP.2006.877651
état	Publié - 1 janv. 2006

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10.1109/TSP.2006.877651

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title = "Kalman filtering in triplet Markov chains",

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author = "Boujemaa Ait-El-Fquih and Fran{\c c}ois Desbouvries",

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AU - Desbouvries, François

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KW - Bayesian signal restoration

KW - Hidden Markov chains Kalman filtering

KW - Markovian models

KW - Triplet Markov chains

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JF - IEEE Transactions on Signal Processing

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Kalman filtering in triplet Markov chains

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