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Bootstrap Techniques for Signal Processing

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ISBN-10: 0521034051

ISBN-13: 9780521034050

Edition: N/A

Authors: Abdelhak M. Zoubir, D. Robert Iskander

List price: $57.99
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Description:

The statistical bootstrap is one of the methods that can be used to calculate estimates of a certain number of unknown parameters of a random process or a signal observed in noise, based on a random sample. Such situations are common in signal processing and the bootstrap is especially useful when only a small sample is available or an analytical analysis is too cumbersome or even impossible. This book covers the foundations of the bootstrap, its properties, its strengths, and its limitations. The authors focus on bootstrap signal detection in Gaussian and non-Gaussian interference as well as bootstrap model selection. The theory developed in the book is supported by a number of practical examples written in MATLAB. The book is aimed at graduate students and engineers, and includes applications to real-world problems in areas such as radar and sonar, biomedical engineering, and automotive engineering.
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Book details

List price: $57.99
Publisher: Cambridge University Press
Publication date: 2/15/2007
Binding: Paperback
Pages: 232
Size: 6.69" wide x 9.61" long x 0.47" tall
Weight: 0.836
Language: English

Abdelhak M. Zoubir received the Dipl.-Ing degree (BSc/BEng) from Fachhochschule Niederrhein, Germany, in 1983, the Dipl.-Ing. (MSc/MEng) and the Dr.-Ing. (PhD) degree from Ruhr University Bochum, Germany, in 1987 and 1992, all in Electrical Engineering. Early placement in industry (Kl�ckner-Moeller and Siempelkamp AG) was then followed by Associate Lectureship in the Division for Signal Theory at Ruhr University Bochum, Germany. In June 1992, he joined Queensland University of Technology where he was Lecturer, Senior Lecturer and then Associate Professor in the School of Electrical and Electronic Systems Engineering. In March 1999, he took up the position of Professor of Telecommunications at Curtin University of Technology, where he was Head of the School of Electrical & Computer Engineering from November 2001 until February 2003. In February 2003 he took up the position of Professor in Signal Processing at Darmstadt University of Technology. Dr Zoubir's general research interest lies in statistical methods for signal processing with applications in communications, sonar, radar, biomedical engineering and vibration analysis. His current research interest lies in robust estimation and in bootstrap techniques for spectrum estimation and the modelling of non-stationary and non-Gaussian signals. He was the General Co-Chairman of the Third IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) held in Darmstadt in December 2003, the Technical Chairman of the 11th IEEE Workshop on Statistical Signal processing held in Singapore in August 2001 and Deputy Technical Chairman (Special Sessions/Tutorials) of ICASSP-94 held in Adelaide. He served as an Associate Editor of the IEEE Transactions on Signal Processing from 1999 until 2005 and is currently Associate Editor of the EURASIP journals Signal Processing and the Journal of Applied Signal Processing. Dr Zoubir is a Member of the IEEE SPS Technical Committees on Signal Processing Theory and M

D. Robert Iskander received his Ph.D. from Queensland University of Technology. He is currently a senior lecturer in the School of Engineering at Griffith University, Australia. He is also a visiting research fellow in the Centre for Eye Research at Queensland University of Technology. He has published over 50 technical papers, in fields such as statistical signal processing, visual optics, and ophthalmic instrumentation.

Preface
Notations
Introduction
The bootstrap principle
The principle of resampling
Some theoretical results for the mean
Examples of non-parametric bootstrap estimation
The parametric bootstrap
Bootstrap resampling for dependent data
Examples of dependent data bootstrap estimation
The principle of pivoting and variance stabilisation
Some examples
Limitations of the bootstrap
Trends in bootstrap resampling
Summary
Signal detection with the bootstrap
Principles of hypothesis testing
Sub-optimal detection
Hypothesis testing with the bootstrap
The role of pivoting
Variance estimation
Detection through regression
The bootstrap matched filter
Tolerance interval bootstrap matched filter
Summary
Bootstrap model selection
Preliminaries
Model selection
Model selection in linear models
Model selection based on prediction
Bootstrap based model selection
A consistent bootstrap method
Dependent data in linear models
Model selection in nonlinear models
Data model
Use of bootstrap in model selection
Order selection in autoregressions
Detection of sources using bootstrap techniques
Bootstrap based detection
Null distribution estimation
Bias correction
Simulations
Summary
Real data bootstrap applications
Optimal sensor placement for knock detection
Motivation
Data model
Bootstrap tests
The experiment
Confidence intervals for aircraft parameters
Introduction
Results with real passive acoustic data
Landmine detection
Noise floor estimation in over-the-horizon radar
Principle of the trimmed mean
Optimal trimming
Noise floor estimation
Model order selection for corneal elevation
Summary
Matlab codes for the examples
Basic non-parametric bootstrap estimation
The parametric bootstrap
Bootstrap resampling for dependent data
The principle of pivoting and variance stabilisation
Limitations of bootstrap procedure
Hypothesis testing
The bootstrap matched filter
Bootstrap model selection
Noise floor estimation
Bootstrap Matlab Toolbox
References
Index