
Data Fusion Mathematics
Theory and Practice
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Data Fusion Mathematics
Theory and Practice
About this book
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 multi?target angle?only tracking, and multi?sensor 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 and applications.
This text is geared toward researchers, scientists, teachers, and practicing engineers interested in and working in the multi?sensor data fusion area.
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Information
Table of contents
- Cover
- Half Title
- Title Page
- Copyright Page
- Dedication
- Table of Contents
- Preface
- Acknowledgments
- Authors
- Introduction
- Chapter 1 Introduction to Data Fusion Process
- Chapter 2 Statistics, Probability Models, and Reliability: Towards Probabilistic Data Fusion
- Chapter 3 Fuzzy Logic and Possibility Theory Based Fusion
- Chapter 4 Filtering, Target-Tracking, and Kinematic Data Fusion
- Chapter 5 Decentralized Data Fusion Systems
- Chapter 6 Component Analysis and Data Fusion
- Chapter 7 Image Algebra and Image Fusion
- Chapter 8 Decision Theory and Fusion
- Chapter 9 Wireless Sensor Networks and Multimodal Data Fusion
- Chapter 10 Soft Computing Approaches to Data Fusion
- Chapter 11 Machine Learning in Data Fusion
- Chapter 12 Target Localization Using Network of 2D Ground Radars
- Chapter 13 Multi-Target Angle Only Tracking Using Thermal Imaging Sensors
- Chapter 14 Multi-Sensor Data Fusion for Single Platform and Team of Platforms
- Appendix A: Some More Algorithms
- Appendix B: More Methods of DF and Performance Evaluation Metrics
- Appendix C: Automatic Data Fusion
- Appendix D: Information on Data Fusion Software Tools
- Appendix E: Definitions of Sensor Data Fusion
- Appendix F: Some Research Topics in Data Fusion
- Index