Sensor and data fusion concepts and applications
Author(s)
Bibliographic Information
Sensor and data fusion concepts and applications
（Tutorial texts in optical engineering, v. TT 35）
SPIE, c1999
2nd ed
- :pbk
Available at 4 libraries
Note
Includes bibliographical references and index
Description and Table of Contents
Description
First published in 1993, this "Tutorial Text" has been revised and updated to provide explanations and examples of data fusion algorithms in areas not covered in the first edition. These include Bayesian inference, artificial neural networks and fuzzy logic. All of the chapters in the first edition have been revised and updated and new material is included on the FASCODE and MODTRAN atmospheric models, and EOSAEL, which analyzes physical processes that affect the performance of millimeter-wave and IR sensors.
Table of Contents
- Multiple Sensor System Applications, Benefits, and Atmospheric Attenuation
- Data Fusion Algorithms and Architectures
- Bayesian Inference
- Dempster-Shafer Algorithm
- Artificial Neural Networks
- Voting Fusion
- Fuzzy Logic and Neural Networks
- Passive Data Association Techniques for Unambiguous Location of Targets. Appendices: Planck Radiation Law and Radiative Transfer
- Voting Fusion With Nested Confidence Levels.
by "Nielsen BookData"