Multiresolutional filtering of a class of dynamic multiscale system subject to colored state equation noise

Peiling Cui; Quan Pan; Guizeng Wang; Jianfeng Cui

doi:10.1007/11502593_18

Multiresolutional filtering of a class of dynamic multiscale system subject to colored state equation noise

Peiling Cui, Quan Pan, Guizeng Wang, Jianfeng Cui

School of Automation

Research output: Contribution to journal › Conference article › peer-review

4 Scopus citations

Abstract

In this paper, modeling and estimation of a class of dynamic multiscale system subject to colored state equation noise is proposed. The colored state noise vector is augmented in the system state variables, the state space projection equation is used to link the scales, and then a new system model is built. The new model is in a form suitable for the application of the Kalman filter equations. Haar-wavelet-based model and estimation algorithm are given. Monte Carlo simulation results demonstrate that the proposed algorithm is effective and powerful in this kind of multiscale estimation problem.

Original language	English
Pages (from-to)	218-227
Number of pages	10
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3560
DOIs	https://doi.org/10.1007/11502593_18
State	Published - 2005
Event	First IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2005 - Marina del Rey, CA, United States Duration: 30 Jun 2005 → 1 Jul 2005

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10.1007/11502593_18

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title = "Multiresolutional filtering of a class of dynamic multiscale system subject to colored state equation noise",

abstract = "In this paper, modeling and estimation of a class of dynamic multiscale system subject to colored state equation noise is proposed. The colored state noise vector is augmented in the system state variables, the state space projection equation is used to link the scales, and then a new system model is built. The new model is in a form suitable for the application of the Kalman filter equations. Haar-wavelet-based model and estimation algorithm are given. Monte Carlo simulation results demonstrate that the proposed algorithm is effective and powerful in this kind of multiscale estimation problem.",

author = "Peiling Cui and Quan Pan and Guizeng Wang and Jianfeng Cui",

year = "2005",

doi = "10.1007/11502593\_18",

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Multiresolutional filtering of a class of dynamic multiscale system subject to colored state equation noise. / Cui, Peiling; Pan, Quan; Wang, Guizeng et al.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3560, 2005, p. 218-227.

Research output: Contribution to journal › Conference article › peer-review

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T1 - Multiresolutional filtering of a class of dynamic multiscale system subject to colored state equation noise

AU - Cui, Peiling

AU - Pan, Quan

AU - Wang, Guizeng

AU - Cui, Jianfeng

PY - 2005

Y1 - 2005

N2 - In this paper, modeling and estimation of a class of dynamic multiscale system subject to colored state equation noise is proposed. The colored state noise vector is augmented in the system state variables, the state space projection equation is used to link the scales, and then a new system model is built. The new model is in a form suitable for the application of the Kalman filter equations. Haar-wavelet-based model and estimation algorithm are given. Monte Carlo simulation results demonstrate that the proposed algorithm is effective and powerful in this kind of multiscale estimation problem.

AB - In this paper, modeling and estimation of a class of dynamic multiscale system subject to colored state equation noise is proposed. The colored state noise vector is augmented in the system state variables, the state space projection equation is used to link the scales, and then a new system model is built. The new model is in a form suitable for the application of the Kalman filter equations. Haar-wavelet-based model and estimation algorithm are given. Monte Carlo simulation results demonstrate that the proposed algorithm is effective and powerful in this kind of multiscale estimation problem.

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M3 - 会议文章

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SN - 0302-9743

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JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - First IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2005

Y2 - 30 June 2005 through 1 July 2005

ER -

Multiresolutional filtering of a class of dynamic multiscale system subject to colored state equation noise

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