Robust Statistics for Signal Processing
Robust Statistics for Signal Processing
Visa Koivunen, Aalto University, Finland
Esa Ollila, Aalto University, Finland
Michael Muma, Technische Universität, Darmstadt, Germany
£125.00
GBPUnderstand 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.
- The first ever book on robust signal processing
- Covers important new results and recent developments in robust signal processing
- Includes real-world examples from the authors' experience, demonstrating the relevance of the methods discussed
- Includes the key algorithms in a MATLAB Robust Signal Processing Toolbox, allowing methods to be easily applied
Product details
November 2018Hardback
9781107017412
312 pages
253 × 178 × 18 mm
0.77kg
Available
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.
