This book illustrates the benefits of sensor fusion by considering the characteristics of infrared, microwave, and millimeter-wave sensors, including the influence of the atmosphere on their performance.
Applications that benefit from this technology include:
- vehicular traffic management
- remote sensing
- target classification and tracking
- weather forecasting
- military and homeland defense.
Covering data fusion algorithms in detail, Klein includes a summary of the information required to implement each of the algorithms discussed, and outlines system application scenarios that may limit sensor size but that require high resolution data.
List of Figures
List of Tables
Preface
Chapter 1 : Introduction
Chapter 2 : Multiple Sensor System Applications, Benefits, and Design Considerations
Chapter 3 : Data Fusion Algorithms and Architectures
Chapter 4 : Classical Inference
Chapter 5 : Bayesian Inference
Chapter 6 : Dempster-Shafer Evidential Theory
Chapter 7 : Artificial Neural Networks
Chapter 8 : Voting Logic Fusion
Chapter 9 : Fuzzy Logic and Fuzzy Neural Networks
Chapter 10 : Passive Data Association Techniques for Unambiguous Location of Targets
Chapter 11 : Retrospective Comments
Appendix A : Planck Radiation Law and Radiative Transfer
Appendix B : Voting Fusion with Nested Confidence Levels
Index
