Event-Based State Estimation
A Stochastic Perspective
Nonfiction, Science & Nature, Technology, Automation, Mathematics, Statistics
| Author: | Dawei Shi, Ling Shi, Tongwen Chen | ISBN: | 9783319266060 |
| Publisher: | Springer International Publishing | Publication: | November 19, 2015 |
| Imprint: | Springer | Language: | English |
| Author: | Dawei Shi, Ling Shi, Tongwen Chen |
| ISBN: | 9783319266060 |
| Publisher: | Springer International Publishing |
| Publication: | November 19, 2015 |
| Imprint: | Springer |
| Language: | English |
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**.**
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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**.**
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