Cargando…

Globally Optimal Distributed Kalman Filtering for Multisensor Systems with Unknown Inputs

In this paper, the state estimation for dynamic system with unknown inputs modeled as an autoregressive AR (1) process is considered. We propose an optimal algorithm in mean square error sense by using difference method to eliminate the unknown inputs. Moreover, we consider the state estimation for...

Descripción completa

Detalles Bibliográficos
Autores principales: Ruan, Yali, Luo, Yingting, Zhu, Yunmin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165186/
https://www.ncbi.nlm.nih.gov/pubmed/30200637
http://dx.doi.org/10.3390/s18092976
_version_ 1783359777457307648
author Ruan, Yali
Luo, Yingting
Zhu, Yunmin
author_facet Ruan, Yali
Luo, Yingting
Zhu, Yunmin
author_sort Ruan, Yali
collection PubMed
description In this paper, the state estimation for dynamic system with unknown inputs modeled as an autoregressive AR (1) process is considered. We propose an optimal algorithm in mean square error sense by using difference method to eliminate the unknown inputs. Moreover, we consider the state estimation for multisensor dynamic systems with unknown inputs. It is proved that the distributed fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurement; therefore, it achieves the best performance. The computation complexity of the traditional augmented state algorithm increases with the augmented state dimension. While, the new algorithm shows good performance with much less computations compared to that of the traditional augmented state algorithms. Moreover, numerical examples show that the performances of the traditional algorithms greatly depend on the initial value of the unknown inputs, if the estimation of initial value of the unknown input is largely biased, the performances of the traditional algorithms become quite worse. However, the new algorithm still works well because it is independent of the initial value of the unknown input.
format Online
Article
Text
id pubmed-6165186
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-61651862018-10-10 Globally Optimal Distributed Kalman Filtering for Multisensor Systems with Unknown Inputs Ruan, Yali Luo, Yingting Zhu, Yunmin Sensors (Basel) Article In this paper, the state estimation for dynamic system with unknown inputs modeled as an autoregressive AR (1) process is considered. We propose an optimal algorithm in mean square error sense by using difference method to eliminate the unknown inputs. Moreover, we consider the state estimation for multisensor dynamic systems with unknown inputs. It is proved that the distributed fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurement; therefore, it achieves the best performance. The computation complexity of the traditional augmented state algorithm increases with the augmented state dimension. While, the new algorithm shows good performance with much less computations compared to that of the traditional augmented state algorithms. Moreover, numerical examples show that the performances of the traditional algorithms greatly depend on the initial value of the unknown inputs, if the estimation of initial value of the unknown input is largely biased, the performances of the traditional algorithms become quite worse. However, the new algorithm still works well because it is independent of the initial value of the unknown input. MDPI 2018-09-06 /pmc/articles/PMC6165186/ /pubmed/30200637 http://dx.doi.org/10.3390/s18092976 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ruan, Yali
Luo, Yingting
Zhu, Yunmin
Globally Optimal Distributed Kalman Filtering for Multisensor Systems with Unknown Inputs
title Globally Optimal Distributed Kalman Filtering for Multisensor Systems with Unknown Inputs
title_full Globally Optimal Distributed Kalman Filtering for Multisensor Systems with Unknown Inputs
title_fullStr Globally Optimal Distributed Kalman Filtering for Multisensor Systems with Unknown Inputs
title_full_unstemmed Globally Optimal Distributed Kalman Filtering for Multisensor Systems with Unknown Inputs
title_short Globally Optimal Distributed Kalman Filtering for Multisensor Systems with Unknown Inputs
title_sort globally optimal distributed kalman filtering for multisensor systems with unknown inputs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165186/
https://www.ncbi.nlm.nih.gov/pubmed/30200637
http://dx.doi.org/10.3390/s18092976
work_keys_str_mv AT ruanyali globallyoptimaldistributedkalmanfilteringformultisensorsystemswithunknowninputs
AT luoyingting globallyoptimaldistributedkalmanfilteringformultisensorsystemswithunknowninputs
AT zhuyunmin globallyoptimaldistributedkalmanfilteringformultisensorsystemswithunknowninputs