Multi-sensor data fusion with MATLAB
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Multi-sensor data fusion with MATLAB

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Published by Taylor & Francis in Boca Raton .
Written in English


Book details:

Edition Notes

StatementJitendra R. Raol
Classifications
LC ClassificationsTA331 .R36 2010
The Physical Object
Paginationp. cm.
ID Numbers
Open LibraryOL24520825M
ISBN 109781439800034
LC Control Number2009041607

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Using MATLAB ® examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion. The authors elucidate DF strategies, algorithms, and performance evaluation mainly for aerospace 4/4(2). Book Description. Using MATLAB ® examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion. The authors elucidate DF strategies, algorithms, and performance evaluation mainly for . Summary. Using MATLAB ® examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion. The authors elucidate DF strategies, algorithms, and performance evaluation mainly for . Multi-sensor data fusion with MATLAB / Jitendra R. Raol. p. cm. “A CRC title.” Includes bibliographical references and index. ISBN (hardcover: alk. paper) 1. Multisensor data fusion— Data processing. 2. MATLAB. 3. Detectors. I. Title. TAR36 ’.2—dc22 Visit the Taylor & Francis Web site at.

Using MATLAB examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion. Data Fusion: Concepts and Ideas Intended to be self-contained, Data Fusion provides a comprehensive introduction to the concepts of multi-sensor data fusion. No previous knowledge of multi-sensor data fusion is assumed, although some familiarity with the basic tools of linear algebra, calculus, and simple probability is recommended. Description: Using MATLAB® examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion. The authors elucidate DF strategies, algorithms, and. Multi-Sensor Data Fusion with MATLAB Jitendra R. Raol Using MATLAB® examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion.

Using MATLAB examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion. The authors elucidate DF strategies, algorithms, and performance evaluation mainly. Download Citation | Multi-sensor data fusion with MATLAB® | Using MATLAB® examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion. Multi-sensor data fusion with MATLAB. [J R Raol] Explores the theory and concepts of multi-sensor data fusion, including kinematic data fusion, fuzzy logic and decision fusion, and pixel/image-level fusion. View this book online, via CRCNetBase, both on- and off-campus (please use Desktop Anywhere for off-campus access).   Using MATLAB examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion. The authors elucidate DF strategies, algorithms, and performance evaluation mainlyCited by: