Discrete stochastic processes and optimal filtering
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 pro...
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| Format: | eBook |
| Language: | English |
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London, U.K.
ISTE
2010
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| Edition: | 2nd ed |
| Series: | Digital signal and image processing series
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| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- 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
- Includes bibliographical references and index
- 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
- 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