Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays

This paper is concerned with the optimal Kalman filtering problem for a class of discrete stochastic systems with multiplicative noises and random two-step sensor delays. Three Bernoulli distributed random variables with known conditional probabilities are introduced to characterize the phenomena of...

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Main Authors: Dongyan Chen, Yonglong Yu, Long Xu, Xiaohui Liu
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2015/809734
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spelling doaj-0068882cc46b466393d62f12d9efbca22020-11-24T23:43:25ZengHindawi LimitedDiscrete Dynamics in Nature and Society1026-02261607-887X2015-01-01201510.1155/2015/809734809734Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor DelaysDongyan Chen0Yonglong Yu1Long Xu2Xiaohui Liu3Department of Applied Mathematics, Harbin University of Science and Technology, Harbin 150080, ChinaDepartment of Applied Mathematics, Harbin University of Science and Technology, Harbin 150080, ChinaDepartment of Applied Mathematics, Harbin University of Science and Technology, Harbin 150080, ChinaDepartment of Computer Science, Brunel University London, Uxbridge, Middlesex UB8 3PH, UKThis paper is concerned with the optimal Kalman filtering problem for a class of discrete stochastic systems with multiplicative noises and random two-step sensor delays. Three Bernoulli distributed random variables with known conditional probabilities are introduced to characterize the phenomena of the random two-step sensor delays which may happen during the data transmission. By using the state augmentation approach and innovation analysis technique, an optimal Kalman filter is constructed for the augmented system in the sense of the minimum mean square error (MMSE). Subsequently, the optimal Kalman filtering is derived for corresponding augmented system in initial instants. Finally, a simulation example is provided to demonstrate the feasibility and effectiveness of the proposed filtering method.http://dx.doi.org/10.1155/2015/809734
collection DOAJ
language English
format Article
sources DOAJ
author Dongyan Chen
Yonglong Yu
Long Xu
Xiaohui Liu
spellingShingle Dongyan Chen
Yonglong Yu
Long Xu
Xiaohui Liu
Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays
Discrete Dynamics in Nature and Society
author_facet Dongyan Chen
Yonglong Yu
Long Xu
Xiaohui Liu
author_sort Dongyan Chen
title Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays
title_short Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays
title_full Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays
title_fullStr Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays
title_full_unstemmed Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays
title_sort kalman filtering for discrete stochastic systems with multiplicative noises and random two-step sensor delays
publisher Hindawi Limited
series Discrete Dynamics in Nature and Society
issn 1026-0226
1607-887X
publishDate 2015-01-01
description This paper is concerned with the optimal Kalman filtering problem for a class of discrete stochastic systems with multiplicative noises and random two-step sensor delays. Three Bernoulli distributed random variables with known conditional probabilities are introduced to characterize the phenomena of the random two-step sensor delays which may happen during the data transmission. By using the state augmentation approach and innovation analysis technique, an optimal Kalman filter is constructed for the augmented system in the sense of the minimum mean square error (MMSE). Subsequently, the optimal Kalman filtering is derived for corresponding augmented system in initial instants. Finally, a simulation example is provided to demonstrate the feasibility and effectiveness of the proposed filtering method.
url http://dx.doi.org/10.1155/2015/809734
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