3 edition of Multi-sensor data fusion with MATLAB found in the catalog.
Multi-sensor data fusion with MATLAB
J. R. Raol
Published
2010
by Taylor & Francis in Boca Raton
.
Written in English
Edition Notes
| Statement | Jitendra R. Raol. |
| Classifications | |
|---|---|
| LC Classifications | TA331 .R36 2010 |
| The Physical Object | |
| Pagination | p. cm. |
| ID Numbers | |
| Open Library | OL23861566M |
| ISBN 10 | 9781439800034 |
| LC Control Number | 2009041607 |
Multi-Sensor Data Fusion with MATLAB (R) by Jitendra R. Raol, , available at Book Depository with free delivery worldwide. 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- Pages:
Sensor fusion is combining of sensory data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individually. The term uncertainty reduction in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, such as stereoscopic vision . This textbook provides a comprehensive introduction to the theories and techniques of multi-sensor data fusion. It is aimed at advanced undergraduate and first-year graduate students in electrical engineering and computer science, as well as researchers and professional engineers. The book is intended to be self-contained.
MATLAB is used to solve numerous examples in the book. Features. Presents physical principles and analytical methods for sensors using simple mathematics; Contains material that is appropriate for mechanical, aerospace, and electrical engineering courses; Covers wireless sensor networks, MEMS sensors, and multi-sensor data fusion. Multi-Sensor Data Fusion with MATLAB by Jitendra R. Raol Download eBook Multi-Sensor Data Fusion with MATLAB Jitendra R. Raol ebook Page: Publisher: ISBN: , Format: pdf Dec 6, - Especially with the moving forward of urbanization process, the demand for painting robot.
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Multi-Sensor Data Fusion with MATLAB Written for scientists and researchers, this book 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-level 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. Multi-sensor data fusion with MATLAB book The authors elucidate DF strategies, algorithms, and performance evaluation mainly for aerospace Cited by: 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. “A CRC title.” Includes bibliographical references and index. ISBN (hardcover: alk. paper) 1. Multisensor data fusion— Data processing.
MATLAB. Detectors. 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: Multi-Sensor Data Fusion with MATLAB - Ebook written by Jitendra R. Raol. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read 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 (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 Price: $ Those who downloaded this book also downloaded the following books: Comments. 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 purpose of this book is to provide an introduction to the theories and techniques of multi-sensor data fusion. The book has been designed as a text for a one-semester graduate course in multi-sensor data fusion.
It should also be useful to advanced undergraduates in electrical engineering or computerFile Size: KB. The purpose of this book is to provide an introduction to the theories and techniques of multi-sensor data fusion. The book has been designed as a text for a one-semester graduate course in multi-sensor data fusion.
It should also be useful to advanced undergraduates in. This textbook provides a comprehensive introduction to the concepts and idea of multisensor data fusion. It is an extensively revised second edition of the author's successful book: "Multi-Sensor Data Fusion: An Introduction" which was originally published by.
SensorFusion. A simple Matlab example of sensor fusion using a Kalman filter. To run, just launch Matlab, change your directory to where you put the repository, and do.
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 4/5(2).
