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Discrete stochastic processes and optimal filtering

Author: Jean-Claude Bertein; Roger Ceschi
Publisher: London, U.K. : ISTE ; Hoboken, N.J. : John Wiley, 2010.
Series: Digital signal and image processing series.
Edition/Format:   eBook : Document : English : 2nd edView all editions and formats
Summary:
Optimal filtering applied to stationary and non-stationary signals provides the most efficient means of dealing with problems arising from the extraction of noise signals. Moreover, it is a fundamental feature in a range of applications, such as in navigation in aerospace and aeronautics, filter processing in the telecommunications industry, etc. This book provides a comprehensive overview of this area, discussing  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Bertein, Jean-Claude.
Discrete stochastic processes and optimal filtering.
London, U.K. : ISTE ; Hoboken, N.J. : John Wiley, 2010
(DLC) 2009038813
(OCoLC)456420541
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Jean-Claude Bertein; Roger Ceschi
ISBN: 9781118600481 1118600487 9781118600351 1118600355 9781118600535 1118600533
OCLC Number: 827944789
Notes: Translated from French.
Description: 1 online resource (xii, 287 pages) : illustrations.
Contents: Cover; Discrete Stochastic Processes and Optimal Filtering; Title Page; Copyright Page; Table of Contents; Preface ; Introduction ; Chapter 1. Random Vectors ; 1.1. Definitions and general properties ; 1.2. Spaces L1 (dP) and L2 (dP) ; 1.2.1. Definitions ; 1.2.2. Properties ; 1.3. Mathematical expectation and applications. 1.3.1. Definitions 1.3.2. Characteristic functions of a random vector ; 1.4. Second order random variables and vectors ; 1.5. Linear independence of vectors of L2 (dP) ; 1.6. Conditional expectation (concerning random vectors with density function) ; 1.7. Exercises for Chapter 1. Chapter 2. Gaussian Vectors 2.1. Some reminders regarding random Gaussian vectors ; 2.2. Definition and characterization of Gaussian vectors ; 2.3. Results relative to independence ; 2.4. Affine transformation of a Gaussian vector ; 2.5. The existence of Gaussian vectors. 2.6. Exercises for Chapter 2 Chapter 3. Introduction to Discrete Time Processes ; 3.1. Definition ; 3.2. WSS processes and spectral measure ; 3.2.1. Spectral density ; 3.3. Spectral representation of a WSS process ; 3.3.1. Problem ; 3.3.2. Results. 3.4. Introduction to digital filtering 3.5. Important example: autoregressive process ; 3.6. Exercises for Chapter 3 ; Chapter 4. Estimation ; 4.1. Position of the problem ; 4.2. Linear estimation ; 4.3. Best estimate --
conditional expectation.
Series Title: Digital signal and image processing series.
Responsibility: Jean-Claude Bertein, Roger Ceschi.
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Abstract:

Optimal filtering applied to stationary and non-stationary signals provides the most efficient means of dealing with problems arising from the extraction of noise signals. Moreover, it is a  Read more...

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