Event-Based State Estimation: A Stochastic Perspective [Paperback]

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.

 

Introduction.- Linear Gaussian Systems and Event-Based State Estimation.- Event-Triggered Sampling.- Approximate Optimal Filtering Approaches.- Constrained Optimization Approach.- Set-Valued Filtering Approach.- Probabilistic Approach.- Communications Rate Analysis.- Open Problems.- Appendices: Brief Review of Probability Theory; Linear Estimation Theory.

This book addresses essentially the engineers (or researchers and students interested in the area of event-trigged systems). The subject turns around event-based estimation problems in a stochastic setting. This document is self-contained and it is readable with just a basic knowledge of probability theory, Kalman filtering tl%