Kalman filtering in triplet Markov chains

被引：30
|
作者：
Ait-El-Fquih, Boujernaa [1 ]
Desbouvries, Francois
机构：
[1] Inst Natl Telecommun, Dept CITI, F-91011 Evry, France
[2] CNRS, UMR 5157, F-91011 Evry, France
来源：
IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2006年 / 54卷 / 08期
关键词：
Bayesian signal restoration; hidden Markov chains; Kalman filtering; Markovian models; triplet Markov chains;
D O I：
10.1109/TSP.2006.877651
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Let x = {x(n)}(n is an element of IN) be a hidden process, y = {y(n))(n is an element of IN) an observed process, and r = {rn(}n is an element of IN) 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.
引用
收藏
页码：2957 / 2963
页数：7
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