Enhanced distributed estimation based on prior information

Haris M. Khalid; Jimmy C.H. Peng; Magdi S. Mahmoud

doi:10.1049/iet-spr.2014.0029

Enhanced distributed estimation based on prior information

Haris M. Khalid
, Jimmy C.H. Peng
, Magdi S. Mahmoud

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

In this paper, a distributed estimation algorithm using Bayesian-based forward backward Kalman filter (KF) is proposed for stochastic singular linear systems. The method incorporates generalised versions of KF for bounded cases with complete and incomplete prior information, followed by estimation fusion of these cases. The incorporated filters remain optimal given the cross-covariance of the local estimates. The proposed approach is validated on a coupled-tank system.

Original language	English
Pages (from-to)	60-72
Number of pages	13
Journal	IET Signal Processing
Volume	9
Issue number	1
DOIs	https://doi.org/10.1049/iet-spr.2014.0029
State	Published - 1 Feb 2015

Bibliographical note

Publisher Copyright:
© The Institution of Engineering and Technology 2015.

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering

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10.1049/iet-spr.2014.0029

Cite this

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title = "Enhanced distributed estimation based on prior information",

abstract = "In this paper, a distributed estimation algorithm using Bayesian-based forward backward Kalman filter (KF) is proposed for stochastic singular linear systems. The method incorporates generalised versions of KF for bounded cases with complete and incomplete prior information, followed by estimation fusion of these cases. The incorporated filters remain optimal given the cross-covariance of the local estimates. The proposed approach is validated on a coupled-tank system.",

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AB - In this paper, a distributed estimation algorithm using Bayesian-based forward backward Kalman filter (KF) is proposed for stochastic singular linear systems. The method incorporates generalised versions of KF for bounded cases with complete and incomplete prior information, followed by estimation fusion of these cases. The incorporated filters remain optimal given the cross-covariance of the local estimates. The proposed approach is validated on a coupled-tank system.

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Enhanced distributed estimation based on prior information

Abstract

Bibliographical note

ASJC Scopus subject areas

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