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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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 |
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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 |
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