Robust Statistics for Signal Processing

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.

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.

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.