Data Fusion Mathematics: Theory and Practice [Hardcover]

Data Fusion Mathematics: Theory and Practice?offers a comprehensive overview of data fusion (DF) and provides a proper and adequate understanding of the basic mathematics directly related to DF.?

1. Introduction to Data Fusion Process. 2. Statistics, Probability Models, and Reliability: Towards Probabilistic Data Fusion. 3. Fuzzy Logic and Possibility Theory Based Fusion. 4. Filtering, TargetTracking, and Kinematic Data Fusion. 5. Decentralized Data Fusion Systems. 6. Component Analysis and Data Fusion. 7. Image Algebra and Image Fusion. 8. Decision Theory and Fusion. 9. Wireless Sensor Networks and Multimodal Data Fusion. 10. Soft Computing Approaches to Data Fusion. 11. Machine Learning in Data Fusion. 12. Target Localization Using Network of 2D Ground Radars. 13. MultiTarget Angle Only Tracking Using Thermal Imaging Sensors. 14. MultiSensor Data Fusion for Single Platform and Team of Platforms.

Data Fusion Mathematics: Theory and Practice offers a comprehensive overview of data fusion (DF) and provides a proper and adequate understanding of the basic mathematics directly related to DF.

This new edition offers updated chapters alongside four new chapters that are based on recent research carried out by the authors, including topics on machine learning techniques, target localization using a network of 2D ground radar, thermal imaging sensors for multitarget angleonly tracking, and multisensor data fusion for a single platform and team platforms. This book also covers major mathematical expressions, formulae and equations, and, where feasible, their derivations. It discusses signed distance function concepts, DF models and architectures, aspects and methods of types 1 and 2 fuzzy logics, and related practical applications. In addition, the authors cover soft computing paradigms that are finding increasing applications in multi-sensory DF approaches alC.