Sensor and Data Fusion: A Tool for Information Assessment and Decision Making
eBook - PDF

Sensor and Data Fusion: A Tool for Information Assessment and Decision Making

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Sensor and Data Fusion: A Tool for Information Assessment and Decision Making

About this book

The information in the second edition of this volume has been substantially expanded and updated to incorporate recent approaches to sensor and data fusion, as well as additional application examples. A new chapter about data fusion issues associated with multiple-radar tracking systems has also been added. This chapter includes topics such as sensor registration requirements, Kalman filtering, and a discussion of interacting multiple models. As in the first edition, the book discusses the benefits of sensor fusion that accrue when sensors that operate with different phenomenologies or surveil separate volumes of space are used to gather signatures and data about objects or events in their field of view. Subject matter includes: (1) applications of multiple-sensor systems to vehicular traffic management, target classification and tracking, military and homeland defense, and battlefield assessment; (2) target, background, and atmospheric signature-generation phenomena and modeling; (3) the JDL data fusion model; (4) sensor fusion architectures; and (5) detailed descriptions of algorithms that combine multiple-sensor data from target identity and tracking data fusion architectures. Bayesian, Dempster-Shafer, artificial neural networks, fuzzy logic, voting logic, and passive data association techniques for unambiguous location of targets are among the data fusion techniques that are explored.

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Yes, you can access Sensor and Data Fusion: A Tool for Information Assessment and Decision Making by Klein, Lawrence A. in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Civil Engineering. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Table of Contents
  2. List of Figures
  3. List of Tables
  4. Preface
  5. Chapter 1: Introduction
  6. Chapter 2: Multiple-Sensor SystemApplications, Benefits, andDesign Considerations
  7. Chapter 3: Sensor and Data Fusion Architectures and Algorithms
  8. Chapter 4: Classical Inference
  9. Chapter 5: Bayesian Inference
  10. Chapter 6: Dempster–Shafer Evidential Theory
  11. Chapter 7: Artificial Neural Networks
  12. Chapter 8: Voting Logic Fusion
  13. Chapter 9: Fuzzy Logic and Fuzzy NeuralNetworks
  14. Chapter 10: Data Fusion Issues Associatedwith Multiple-Radar TrackingSystems
  15. Chapter 11: Passive Data Association Techniques for UnambiguousLocation of Targets
  16. Chapter 12: Retrospective Comments
  17. Appendix A: Planck Radiation Law and Radiative Transfer
  18. Appendix B: Voting Fusion with Nested Confidence Levels
  19. Appendix C: The Fundamental Matrix of aFixed Continuous-Time System
  20. Index
  21. About the Author