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Event-based state estimation: a stochastic perspective
This book explores event-based estimation problems. It shows how several stochastic approaches are developed to maintain estimation performance when sensors perform their updates at slower rates only when needed. The self-contained presentation makes this book suitable for readers with no more than...
Autores principales: | , , |
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Lenguaje: | eng |
Publicado: |
Springer
2016
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-3-319-26606-0 http://cds.cern.ch/record/2112846 |
_version_ | 1780948966402162688 |
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author | Shi, Dawei Shi, Ling Chen, Tongwen |
author_facet | Shi, Dawei Shi, Ling Chen, Tongwen |
author_sort | Shi, Dawei |
collection | CERN |
description | This book explores event-based estimation problems. It shows how several stochastic approaches are developed to maintain estimation performance when sensors perform their updates at slower rates only when needed. The self-contained presentation makes this book suitable for readers with no more than a basic knowledge of probability analysis, matrix algebra and linear systems. The introduction and literature review provide information, while the main content deals with estimation problems from four distinct angles in a stochastic setting, using numerous illustrative examples and comparisons. The text elucidates both theoretical developments and their applications, and is rounded out by a review of open problems. This book is a valuable resource for researchers and students who wish to expand their knowledge and work in the area of event-triggered systems. At the same time, engineers and practitioners in industrial process control will benefit from the event-triggering technique that reduces communication costs and improves energy efficiency in wireless automation applications. . |
id | cern-2112846 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2016 |
publisher | Springer |
record_format | invenio |
spelling | cern-21128462021-04-21T20:00:54Zdoi:10.1007/978-3-319-26606-0http://cds.cern.ch/record/2112846engShi, DaweiShi, LingChen, TongwenEvent-based state estimation: a stochastic perspectiveEngineeringThis book explores event-based estimation problems. It shows how several stochastic approaches are developed to maintain estimation performance when sensors perform their updates at slower rates only when needed. The self-contained presentation makes this book suitable for readers with no more than a basic knowledge of probability analysis, matrix algebra and linear systems. The introduction and literature review provide information, while the main content deals with estimation problems from four distinct angles in a stochastic setting, using numerous illustrative examples and comparisons. The text elucidates both theoretical developments and their applications, and is rounded out by a review of open problems. This book is a valuable resource for researchers and students who wish to expand their knowledge and work in the area of event-triggered systems. At the same time, engineers and practitioners in industrial process control will benefit from the event-triggering technique that reduces communication costs and improves energy efficiency in wireless automation applications. .Springeroai:cds.cern.ch:21128462016 |
spellingShingle | Engineering Shi, Dawei Shi, Ling Chen, Tongwen Event-based state estimation: a stochastic perspective |
title | Event-based state estimation: a stochastic perspective |
title_full | Event-based state estimation: a stochastic perspective |
title_fullStr | Event-based state estimation: a stochastic perspective |
title_full_unstemmed | Event-based state estimation: a stochastic perspective |
title_short | Event-based state estimation: a stochastic perspective |
title_sort | event-based state estimation: a stochastic perspective |
topic | Engineering |
url | https://dx.doi.org/10.1007/978-3-319-26606-0 http://cds.cern.ch/record/2112846 |
work_keys_str_mv | AT shidawei eventbasedstateestimationastochasticperspective AT shiling eventbasedstateestimationastochasticperspective AT chentongwen eventbasedstateestimationastochasticperspective |