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Information-theoretic Signal Processing and Applications Book

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Description

This book explores how information theory helps clarify and suggest new approaches for designing and analyzing statistical signal processing algorithms. It examines the connection between information theory and the design of algorithms used in radar, sonar, imaging, and pattern recognition. The text is designed to expand applicability to new areas of interest within the field. Readers will study principal information-theoretic measures currently in use, including Kullback-Leibler divergence, mutual information, and Fisher information. Numerous examples are provided throughout the text to illustrate these concepts and their practical implementations in signal processing.

Key Features

Explores the link between information theory and signal processing algorithms used in radar, sonar, imaging, and pattern recognition.

Provides detailed study of principal information-theoretic measures such as Kullback-Leibler divergence, mutual information, and Fisher information.

Includes numerous examples to illustrate core concepts and their application to statistical signal processing algorithms.

Designed to expand the applicability of information theory to new problems of interest in the field of computing and information systems.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
28 July 2020
Listed Since
29 July 2020

Barcode

No barcode data available

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