Robust statistics for signal processing

著者

書誌事項

Robust statistics for signal processing

Abdelhak M. Zoubir ... [et al.]

Cambridge University Press, 2018

  • : hardback

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注記

Other authors: Visa Koivunen, Esa Ollila, Michael Muma

Bibliography: p. 272-287

Includes index

内容説明・目次

内容説明

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.

目次

  • 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.

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