Authors:
- Shows the reader which spectral methods (algorithms) are useful in practice
- Demonstrates the clear advantages of using parametric rather than non-parametric models for spectral analysis
- Provides the reader with detailed assistance in using the MATLAB® ARMAsel Toolbox and problems with which to use it
- Teaches the reader a method for obtaining objectively reliable optimal or near-optimal spectral estimates for random data
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Table of contents (10 chapters)
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Front Matter
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Back Matter
About this book
"Automatic Autocorrelation and Spectral Analysis" gives random data a language to communicate the information they contain objectively. It takes advantage of greater computing power and robust algorithms to produce enough candidate models of a given group of data to be sure of providing a suitable one. Improved order selection guarantees that one of the best (often the best) will be selected automatically. Written for graduate signal processing students and for researchers and engineers using time series analysis for applications ranging from breakdown prevention in heavy machinery to measuring lung noise for medical diagnosis, this text offers:
- tuition in how power spectral density and the autocorrelation function of stochastic data can be estimated and interpreted in time series models;
- extensive support for the MATLAB® ARMAsel toolbox;
- applications showing the methods in action;
- appropriate mathematics for students to apply the methods with references for those who wish to develop them further.
Authors and Affiliations
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Department of Multi Scale Physics, Delft University of Technology Kramers Laboratory, Delft, The Netherlands
Piet M. T. Broersen
About the author
Piet M.T. Broersen received the Ph.D. degree in 1976, from the Delft University of Technology in the Netherlands.
He is currently with the Department of Multi-scale Physics at TU Delft. His main research interest is in automatic identification on statistical grounds. He has developed a practical solution for the spectral and autocorrelation analysis of stochastic data by the automatic selection of a suitable order and type for a time series model of the data.
Bibliographic Information
Book Title: Automatic Autocorrelation and Spectral Analysis
Authors: Piet M. T. Broersen
DOI: https://doi.org/10.1007/1-84628-329-9
Publisher: Springer London
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag London 2006
Hardcover ISBN: 978-1-84628-328-4Published: 20 April 2006
Softcover ISBN: 978-1-84996-581-1Published: 13 October 2010
eBook ISBN: 978-1-84628-329-1Published: 02 August 2006
Edition Number: 1
Number of Pages: XII, 298
Number of Illustrations: 104 b/w illustrations
Topics: Engineering, general, Theory of Computation, Signal, Image and Speech Processing, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Computational Intelligence, Image Processing and Computer Vision