Abstract
Let x = {Xn}nεIN be a hidden process, y = {Yn}nεIN an observed process, and r = {rn}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.
Original language | English (US) |
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Pages (from-to) | 2957-2963 |
Number of pages | 7 |
Journal | IEEE Transactions on Signal Processing |
Volume | 54 |
Issue number | 8 |
DOIs | |
State | Published - Aug 2006 |
Externally published | Yes |
Keywords
- Bayesian signal restoration
- Hidden Markov chains Kalman filtering
- Markovian models
- Triplet Markov chains
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering