Approximate Bayesian approach to non-Gaussian estimation in a linear model with dependent state and noise vectors

被引：0
|
作者：
Hoang, HS
Baraille, R
Talagrand, O
DeMey, P
机构：
[1] CNES, CNRS, GRGS, SHOM, F-31401 Toulouse 4, France
[2] CMO, GRGS, SHOM, F-31401 Toulouse, France
[3] ENS, LMD, F-75231 Paris 05, France
来源：
APPLIED MATHEMATICS AND OPTIMIZATION | 2001年 / 43卷 / 03期
关键词：
linear model; non-Gaussian estimation; robust Bayesian estimation;
D O I：
10.1007/s00245-001-0005-y
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper extends the results of Masreliez [8] on the design of non-Gaussian estimators for a more general class of the parameter estimation problem when the system state and the observation noise may be dependent and non-Gaussian simultaneously It is shown that the proposed non-Gaussian algorithms can approximate with high precision the minimum mean square estimator. Application of the approach to the design of different optimal (and stable) estimation algorithms is illustrated. The efficiency of the proposed algorithms is tested in some simulation experiments.
引用
收藏
页码：203 / 220
页数：18
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