Filtering and System Identification

by Verhaegen, michel; Verdult, Vincent

• ISBN13: 9781107405028
• ISBN10: 1107405025
• Format: Paperback
• Copyright: 2012-07-19
• Publisher: Cambridge Univ Pr

Summary

Filtering and system identification are powerful techniques for building models of complex systems. This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models derived using these techniques. Emphasis is on the least squares approach as applied to the linear state-space model, and problems of increasing complexity are analyzed and solved within this framework, starting with the Kalman filter and concluding with the estimation of a full model, noise statistics and state estimator directly from the data. Key background topics, including linear matrix algebra and linear system theory, are covered, followed by different estimation and identification methods in the state-space model. With end-of-chapter exercises, MATLAB simulations and numerous illustrations, this book will appeal to graduate students and researchers in electrical, mechanical and aerospace engineering. It is also useful for practitioners. Additional resources for this title, including solutions for instructors, are available online at www.cambridge.org/9780521875127.

Table of Contents

Preface

Introduction

Linear algebra

Discrete-time signals and systems

Random variables and signals

Kalman filtering

Estimation of spectra and frequency response functions

Output-error parametric model estimation

Prediction-error parametric model estimation

Subspace model identification

The system identification cycle

Notation and symbols

List of abbreviations

References

Index

Table of Contents provided by Publisher. All Rights Reserved.