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Understand the benefits of robust statistics for signal processing using this unique and authoritative text.
List 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.
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.
Summary
Moving from fundamental theory to cutting-edge advances in the field, gain a comprehensive understanding of the benefits that robust statistics bring to signal processing with this authoritative treatment of the subject. Real-world examples and a MATLAB Robust Signal Processing Toolbox allow for easy practical application of the methods described.
