Handbook of Multisensor Data Fusion Theory and Practice 2nd edition by Martin Liggins II, David Hall , James Llinas ISBN 1420053086 978-1420053081

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ISBN 10: 1420053086
ISBN 13: 978-1420053081
Author: Martin Liggins II, David Hall , James Llinas 

n the years since the bestselling first edition, fusion research and applications have adapted to service-oriented architectures and pushed the boundaries of situational modeling in human behavior, expanding into fields such as chemical and biological sensing, crisis management, and intelligent buildings.

Handbook of Multisensor Data Fusion: Theory and Practice, Second Edition represents the most current concepts and theory as information fusion expands into the realm of network-centric architectures. It reflects new developments in distributed and detection fusion, situation and impact awareness in complex applications, and human cognitive concepts. With contributions from the world’s leading fusion experts, this second edition expands to 31 chapters covering the fundamental theory and cutting-edge developments that are driving this field.

New to the Second Edition―

· Applications in electromagnetic systems and chemical and biological sensors

· Army command and combat identification techniques

· Techniques for automated reasoning

· Advances in Kalman filtering

· Fusion in a network centric environment

· Service-oriented architecture concepts

· Intelligent agents for improved decision making

· Commercial off-the-shelf (COTS) software tools

From basic information to state-of-the-art theories, this second edition continues to be a unique, comprehensive, and up-to-date resource for data fusion systems designers.

Handbook of Multisensor Data Fusion: Theory and Practice 2nd Table of contents:

Preface

• Purpose of the Book
• Structure and Scope
• Acknowledgments

Chapter 1: Introduction to Multisensor Data Fusion
1.1. Overview of Data Fusion
1.2. Importance of Multisensor Systems
1.3. Historical Development of Data Fusion
1.4. Applications of Multisensor Data Fusion
1.5. Challenges and Opportunities in the Field
1.6. Basic Definitions and Terminology

Chapter 2: Fundamentals of Sensors and Sensor Networks
2.1. Types of Sensors and Their Characteristics
2.2. Sensor Performance Metrics
2.3. Sensor Network Architectures
2.4. Sensor Data Quality and Calibration
2.5. Sensor Fusion and Data Integration
2.6. Sensor Fusion in Real-Time Applications

Chapter 3: Data Fusion Models and Frameworks
3.1. Classification of Data Fusion Approaches
3.2. Low-Level Fusion: Data-Level Fusion
3.3. Mid-Level Fusion: Feature-Level Fusion
3.4. High-Level Fusion: Decision-Level Fusion
3.5. Fusion Strategies and Methodologies
3.6. Frameworks for Multisensor Data Fusion

Chapter 4: Mathematical Foundations of Data Fusion
4.1. Probability and Statistics in Data Fusion
4.2. Bayesian Inference and Estimation
4.3. Kalman Filtering and Extended Kalman Filter (EKF)
4.4. Particle Filters and Monte Carlo Methods
4.5. Dempster-Shafer Theory of Evidence
4.6. Neural Networks and Machine Learning in Fusion
4.7. Optimization Techniques for Fusion Algorithms

Chapter 5: Multisensor Data Fusion Techniques
5.1. Sensor Data Registration and Alignment
5.2. Kalman Filter-Based Fusion
5.3. Particle Filter-Based Fusion
5.4. Fuzzy Logic and Neuro-Fuzzy Systems
5.5. Principal Component Analysis and Independent Component Analysis
5.6. Data Association and Tracking Methods
5.7. Feature-Based Fusion Techniques

Chapter 6: Sensor Fusion Architectures and Systems
6.1. Centralized vs. Decentralized Fusion Architectures
6.2. Distributed Fusion Systems
6.3. Hierarchical Fusion Architectures
6.4. Real-Time Fusion Architectures and Systems
6.5. Hardware and Software Integration for Sensor Fusion
6.6. Performance Evaluation of Fusion Systems

Chapter 7: Data Fusion in Multi-Agent Systems and Robotics
7.1. Multisensor Fusion for Autonomous Vehicles
7.2. Fusion for Mobile Robotics and Navigation
7.3. Swarm Robotics and Collaborative Fusion
7.4. Sensor Fusion in SLAM (Simultaneous Localization and Mapping)
7.5. Human-Robot Interaction and Data Fusion
7.6. Sensor Fusion in Multi-Agent Systems

Chapter 8: Applications of Multisensor Data Fusion
8.1. Data Fusion in Defense and Security Systems
8.2. Environmental Monitoring and Remote Sensing
8.3. Industrial Applications of Sensor Fusion
8.4. Healthcare and Medical Applications
8.5. Automotive and Transportation Systems
8.6. Smart Cities and IoT Applications

Chapter 9: Data Fusion in Decision Making and Control Systems
9.1. Multisensor Data Fusion in Decision Support Systems
9.2. Decision Making Under Uncertainty
9.3. Control Systems and Sensor Fusion
9.4. Fault Diagnosis and Prognostics
9.5. Reliability and Robustness of Fusion Systems
9.6. Real-Time Decision Making Using Data Fusion

Chapter 10: Advanced Topics in Multisensor Data Fusion
10.1. Deep Learning Approaches for Sensor Fusion
10.2. Fusion of High-Dimensional and Big Data
10.3. Cross-Sensor Data Fusion (e.g., LIDAR, Radar, Cameras)
10.4. Fusion in Complex Environments (Urban, Underwater, etc.)
10.5. Fusion in Cyber-Physical Systems
10.6. Cognitive and Adaptive Fusion Systems

Chapter 11: Case Studies in Multisensor Data Fusion
11.1. Case Study 1: Fusion for Autonomous Driving Systems
11.2. Case Study 2: Sensor Fusion in Surveillance and Security
11.3. Case Study 3: Fusion for Smart Grid Monitoring
11.4. Case Study 4: Medical Sensor Fusion in Patient Monitoring
11.5. Case Study 5: Industrial Sensor Fusion for Predictive Maintenance
11.6. Case Study 6: Remote Sensing and Environmental Data Fusion

Chapter 12: Future Trends in Multisensor Data Fusion
12.1. Integration with Artificial Intelligence and Machine Learning
12.2. Quantum Computing and Its Impact on Data Fusion
12.3. Edge Computing and Distributed Fusion Networks
12.4. Fusion for 5G and Beyond Communication Systems
12.5. Privacy and Security Concerns in Data Fusion
12.6. Ethical Considerations in Multisensor Data Fusion

Appendices
A.1. Glossary of Terms
A.2. List of Symbols and Notations
A.3. Recommended Software Tools for Data Fusion
A.4. MATLAB and Python Code Examples for Data Fusion
A.5. Further Reading and Resources
A.6. Index

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