Robust Statistics for Signal Processing

by Abdelhak M. Zoubir, Visa Koivunen, Esa Ollila, Michael Muma

Overview

Understand the benefits of robust statistics for signal processing with this authoritative yet accessible text. The first ever book on the subject, it provides a comprehensive overview of the field, moving from fundamental theory through to important new results and recent advances. Topics covered include advanced robust methods for complex-valued data, robust covariance estimation, penalized regression models, dependent data, robust bootstrap, and tensors. Robustness issues are illustrated throughout using real-world examples and key algorithms are included in a MATLAB Robust Signal Processing Toolbox accompanying the book online, allowing the methods discussed to be easily applied and adapted to multiple practical situations. This unique resource provides a powerful tool for researchers and practitioners working in the field of signal processing.

Product Details

ISBN-13:	9781108680486
Publisher:	Cambridge University Press
Publication date:	11/08/2018
Sold by:	Barnes & Noble
Format:	eBook
File size:	14 MB
Note:	This product may take a few minutes to download.

About the Author

Abdelhak M. Zoubir is a Professor of Signal Processing and the Head of the Signal Processing Group at Technische Universität, Darmstadt, Germany. He is a Fellow of the IEEE, an IEEE Distinguished Lecturer, and the co-author of Bootstrap Techniques for Signal Processing (Cambridge, 2004).
Visa Koivunen is a Professor of Signal Processing at Aalto University, Finland. He is also a Fellow of the IEEE and an IEEE Distinguished Lecturer.
Esa Ollila is an Associate Professor of Signal Processing at Aalto University, Finland.
Michael Muma is a Postdoctoral Research Fellow in the Signal Processing Group at Technische Universität, Darmstadt, Germany.

Table of Contents

1. Introduction and foundations; 2. Robust estimation: the linear regression model; 3. Robust penalized regression in the linear model; 4. Robust estimation of location and scatter (covariance) matrix; 5. Robustness in sensor array processing; 6. Tensor models and robust statistics; 7. Robust filtering; 8. Robust methods for dependent data; 9. Robust spectral estimation; 10. Robust bootstrap methods; 11. Real-life applications.