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

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SPIE Press, 2004 - Science - 317 pages
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Annotation This book describes the benefits of sensor fusion as illustrated by considering the characteristics of infrared, microwave, and millimeter-wave sensors, including the influence of the atmosphere on their performance, sensor system application scenarios that may limit sensor size but still require high resolution data, and the attributes of data fusion architectures and algorithms. The data fusion algorithms discussed in detail include classical inference, Bayesian inference, Dempster-Shafer evidential theory, artificial neural networks, voting logic as derived from Boolean algebra expressions, fuzzy logic, and detection and tracking of objects using only passively acquired data. A summary is presented of the information required to implement each of the data fusion algorithms discussed. Weather forecasting, Earth resource surveys that use remote sensing, vehicular traffic management, target classification and tracking, military and homeland defense, and battlefield assessment are some of the applications that will benefit from the discussions of signature-generation phenomena, sensor fusion architectures, and data fusion algorithms provided in this text.
  

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Contents

Multiple Sensor System Applications Benefits and Design
7
Data Fusion Algorithms and Architectures
51
Classical Inference
101
Bayesian Inference
127
DempsterShafer Evidential Theory
149
Artificial Neural Networks
183
Voting Logic Fusion
215
Fuzzy Logic and Fuzzy Neural Networks
237
Passive Data Association Techniques for Unambiguous
263
site
287
Retrospective Comments
293
Appendix A Planck Radiation Law and Radiative Transfer
299
Appendix B Voting Fusion with Nested Confidence Levels
307
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Page iv - Data fusion is a multilevel, multifaceted process dealing with the automatic detection, association, correlation, estimation, and combination of data and information from single and multiple sources to achieve refined position and identity estimates, and complete and timely assessments of situations and threats and their significance.

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About the author (2004)

Lawrence A. Klein, Ph.D. received his B.E.E. from the City College of New York, his M.S. in Electrical Engineering from the University of Rochester (NY), and his Ph.D. in Electrical Engineering from New York University. Dr. Klein is currently a private consultant and was commended by the U.S. Federal Highway Administration for his performance while with the Hughes Aircraft Company as principal investigator on the Detection Technology for Intelligent Vehicle Highway Systems program. He is a member of the Freeway Operations Committee of TRB, member of ASTM E17 Group V ITS, senior member of the IEEE, was co-chair of the SPIE Collision Avoidance and Automated Traffic Management Sensors Conference, and has published 3 other books and over 50 technical papers.

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