Robust Statistics for Signal Processing

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

• Publisher: Cambridge University Press
• Year: 2018
• Print ISBN: 9781107017412
• eBook ISBN: 9781108582759
• Topic: Technology & Engineering
• Subtopic: Signals & Signal Processing
• Index: Technology & Engineering

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.

Table of contents

• Cover
• Half-title
• Title page
• Copyright information
• Contents
• Preface
• Abbreviations
• List of Symbols
• 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
• Bibliography
• Index