Generalized Millman's formula and its application for estimation problems

Shin V; Lee Y; Choi TS

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Generalized Millman's formula and its application for estimation problems

机译：广义米尔曼公式及其在估计问题中的应用

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摘要

We derive an optimal combination of arbitrary number correlated estimates. In particular, for two estimates this combination represents the well-known Millman and Bar-Shalom-Campo formulae for uncorrelated and correlated estimation errors, respectively. This new result is applied to the various estimation problems as least-squares estimation, Kalman filtering, and adaptive filtering. The new approximate reduced-order estimators with parallel structure are presented. A practical implementation issue to consider these estimators is also addressed. Examples demonstrate the accuracy and efficiency of application of the generalized Millman's formula. (C) 2005 Elsevier B.V. All rights reserved.

机译：我们得出任意数相关估计的最佳组合。特别是，对于两个估计，此组合分别代表了不相关和相关估计误差的众所周知的Millman和Bar-Shalom-Campo公式。此新结果应用于各种估计问题，例如最小二乘估计，卡尔曼滤波和自适应滤波。提出了具有并行结构的新的近似降阶估计量。还讨论了考虑这些估计量的实际实施问题。实例说明了应用广义Millman公式的准确性和效率。（C）2005 Elsevier B.V.保留所有权利。

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来源
《Signal processing》 |2006年第2期|p. 257-266|共10页
作者
Shin V; Lee Y; Choi TS;
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作者单位

Kyungnam Univ, Dept Math Educ, Masan 631701, South Korea;

Gwangju Inst Sci & Technol, Dept Mechatron, Kwangju 500712, South Korea;

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原文格式 PDF
正文语种 eng
中图分类通信;
关键词
least-squares estimator; Millman's formula; Kalman filter; Lainiotis-Kalman adaptive filter; suboptimal filtering; multisensor; data fusion;

机译：最小二乘估计器;米尔曼公式;卡尔曼滤波器;Lainiotis-卡尔曼自适应滤波器;次优滤波;多传感器;数据融合;

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Generalized Millman's formula and its application for estimation problems

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