|
|
|
|
| LEADER |
04116nmm a2200613 u 4500 |
| 001 |
EB001918154 |
| 003 |
EBX01000000000000001081056 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
210123 ||| eng |
| 020 |
|
|
|a 1118600487
|
| 020 |
|
|
|a 9781118600481
|
| 020 |
|
|
|a 9781118600351
|
| 020 |
|
|
|a 9781118600535
|
| 020 |
|
|
|a 1118600355
|
| 050 |
|
4 |
|a TK5102.9
|
| 100 |
1 |
|
|a Bertein, Jean-Claude
|
| 245 |
0 |
0 |
|a Discrete stochastic processes and optimal filtering
|c Jean-Claude Bertein, Roger Ceschi
|
| 250 |
|
|
|a 2nd ed
|
| 260 |
|
|
|a London, U.K.
|b ISTE
|c 2010
|
| 300 |
|
|
|a xii, 287 pages
|b illustrations
|
| 505 |
0 |
|
|a 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
|
| 505 |
0 |
|
|a 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
|
| 505 |
0 |
|
|a Includes bibliographical references and index
|
| 505 |
0 |
|
|a 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
|
| 505 |
0 |
|
|a 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
|
| 505 |
0 |
|
|a 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
|
| 653 |
|
|
|a Stochastic processes / fast
|
| 653 |
|
|
|a Processus stochastiques
|
| 653 |
|
|
|a Traitement du signal / Mathématiques
|
| 653 |
|
|
|a Signal processing / Mathematics
|
| 653 |
|
|
|a Digital filters (Mathematics) / http://id.loc.gov/authorities/subjects/sh85037977
|
| 653 |
|
|
|a TECHNOLOGY & ENGINEERING / Signals & Signal Processing / bisacsh
|
| 653 |
|
|
|a Digital filters (Mathematics) / fast
|
| 653 |
|
|
|a COMPUTERS / Information Theory / bisacsh
|
| 653 |
|
|
|a Filtres numériques (Mathématiques)
|
| 653 |
|
|
|a Stochastic processes / http://id.loc.gov/authorities/subjects/sh85128181
|
| 653 |
|
|
|a Signal processing / Mathematics / fast
|
| 700 |
1 |
|
|a Ceschi, Roger
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b OREILLY
|a O'Reilly
|
| 490 |
0 |
|
|a Digital signal and image processing series
|
| 500 |
|
|
|a Translated from French
|
| 776 |
|
|
|z 9781848211810
|
| 776 |
|
|
|z 1118600355
|
| 776 |
|
|
|z 1848211813
|
| 776 |
|
|
|z 9781118600351
|
| 776 |
|
|
|z 9781118600481
|
| 776 |
|
|
|z 1118600487
|
| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9781118600535/?ar
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 621.382/2
|
| 082 |
0 |
|
|a 510
|
| 082 |
0 |
|
|a 620
|
| 520 |
|
|
|a 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 random and Gaussian vectors, outlining the results necessary for the creation of Wiener and adaptive filters used for stationary signals, as well as examining Kalman filters which are used in relation to non-stationary signals. Exercises with solutions feature in each chapter to demonstrate the practical application of these ideas using MATLAB.
|