Jitendra R. Raol
Published December 16, 2009
Reference - 568 Pages - 224 B/W Illustrations
ISBN 9781439800034 - CAT# K10025
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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 applications, although the methods can also be applied to systems in other areas, such as biomedicine, military defense, and environmental engineering.
After presenting several useful strategies and algorithms for DF and tracking performance, the book evaluates DF algorithms, software, and systems. It next covers fuzzy logic, fuzzy sets and their properties, fuzzy logic operators, fuzzy propositions/rule-based systems, an inference engine, and defuzzification methods. It develops a new MATLAB graphical user interface for evaluating fuzzy implication functions, before using fuzzy logic to estimate the unknown states of a dynamic system by processing sensor data. The book then employs principal component analysis, spatial frequency, and wavelet-based image fusion algorithms for the fusion of image data from sensors. It also presents procedures for combing tracks obtained from imaging sensor and ground-based radar. The final chapters discuss how DF is applied to mobile intelligent autonomous systems and intelligent monitoring systems.
Fusing sensors’ data can lead to numerous benefits in a system’s performance. Through real-world examples and the evaluation of algorithmic results, this detailed book provides an understanding of MSDF concepts and methods from a practical point of view.
Select MATLAB programs are available for download on www.crcpress.com
Theory of Data Fusion and Kinematic-Level Fusion, J.R. Raol, G. Girija, and N. Shanthakumar
Concepts and Theory of Data Fusion
Strategies and Algorithms for Target Tracking and Data Fusion
Performance Evaluation of Data Fusion Systems, Software, and Tracking
Fuzzy Logic and Decision Fusion, J.R. Raol and S.K. Kashyap
Theory of Fuzzy Logic
Performance Evaluation of Fuzzy Logic-Based Decision Systems
Pixel and Feature-Level Image Fusion, J.R. Raol and V.P.S. Naidu
Pixel- and Feature-Level Image Fusion Concepts and Algorithms
Performance Evaluation of Image-Based Data Fusion Systems
A Brief on Data Fusion in Other Systems, Ajith Gopal and Simukai Utete
Introduction: Overview of Data Fusion in Mobile Intelligent Autonomous Systems
Intelligent Monitoring and Fusion