Discrete Stochastic Processes and Optimal Filtering (Digital Signal & Image Processing Series (ISTE-DSP)) - Info and Reading Options
By Jean-Claude Bertein
"Discrete Stochastic Processes and Optimal Filtering (Digital Signal & Image Processing Series (ISTE-DSP))" was published by ISTE Publishing Company in February 4, 2008 - London, it has 288 pages and the language of the book is English.
“Discrete Stochastic Processes and Optimal Filtering (Digital Signal & Image Processing Series (ISTE-DSP))” Metadata:
- Title: ➤ Discrete Stochastic Processes and Optimal Filtering (Digital Signal & Image Processing Series (ISTE-DSP))
- Author: Jean-Claude Bertein
- Language: English
- Number of Pages: 288
- Publisher: ISTE Publishing Company
- Publish Date: February 4, 2008
- Publish Location: London
“Discrete Stochastic Processes and Optimal Filtering (Digital Signal & Image Processing Series (ISTE-DSP))” Subjects and Themes:
- Subjects: Stochastic processes - Mathematics - Digital filters (Mathematics) - Signal processing - Electric filters
Edition Specifications:
- Format: Hardcover
- Weight: 1.2 pounds
- Dimensions: 9.3 x 6.2 x 0.9 inches
Edition Identifiers:
- The Open Library ID: OL12293494M - OL19843592W
- Online Computer Library Center (OCLC) ID: 855858051
- Library of Congress Control Number (LCCN): 2007009433
- ISBN-13: 9781905209743
- ISBN-10: 1905209746
- All ISBNs: 1905209746 - 9781905209743
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"Discrete Stochastic Processes and Optimal Filtering (Digital Signal & Image Processing Series (ISTE-DSP))" Description:
Open Data:
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
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