An innovation approach to the estimation problem from uncertain observations with correlated signal and noise

被引：0

作者：

Nakamori, S ^{[1
]}

Caballero, R ^{[1
]}

Hermoso, A ^{[1
]}

Jiménez, J ^{[1
]}

Linares, J ^{[1
]}

机构：

[1] Kagoshima Univ, Dept Technol, Kagoshima 8900065, Japan

来源：

ICNAAM 2004: INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2004 | 2004年

关键词：

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper presents recursive filtering and fixed-point smoothing algorithms from uncertain observations, when the variables describing the uncertainty are independent, and the signal and observation white noise are correlated. It is assumed that both the autocovariance function of the signal and the crosscovariance function between the signal and the observation noise are expressed in a semi-degenerate kernel form. The estimators are obtained by an innovation approach and do not use the state-space model but only the covariance information of the signal and the observation noise, and the probability that the signal exists in the observed values. (c) 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

引用

页码：283 / 286

页数：4

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