Sensor and Data Fusion: A Tool for Information Assessment and Decision MakingAnnotation 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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Page xix
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Contents
Chapter | 4 |
2 | 10 |
Definition of an architecture | 15 |
2 | 18 |
8 | 25 |
References | 46 |
Data fusion processor functions | 80 |
Featurelevel fusion | 92 |
9 | 142 |
DempsterShafer Evidential Theory | 149 |
Artificial Neural Networks | 183 |
Voting Logic Fusion | 215 |
Fuzzy Logic and Fuzzy Neural Networks | 237 |
Chapter 10 | 263 |
Chapter 11 | 293 |
Appendix | 299 |
Other editions - View all
Sensor and Data Fusion: A Tool for Information Assessment and Decision Making Lawrence A. Klein No preview available - 2012 |
Sensor and Data Fusion: A Tool for Information Assessment and Decision Making Lawrence A. Klein No preview available - 2004 |
Common terms and phrases
absorption adaptive algorithm allows angle antenna applications approach architecture artificial neural networks assigned association atmospheric Bayesian belief calculated Chapter classification coefficient combined computed conditional confidence levels contains corresponding data fusion decision defined Dempster-Shafer Dempster's rule described detection probability direction distribution elements emitters energy equal error estimation evidence example false alarm probability Figure frequency function fuzzy fuzzy logic fuzzy sets given hypothesis illustrated inference input interval layer learning linear mean measurements method modes multiple sensor normal object observed operating optimal output passive pattern percent performance position probability mass problem processing processor production propositions radar rain range received reduced represents resolution result rule sample sample mean selected sensor resolution shown signal signatures solution sources space statistical Table target techniques temperature theory threshold tracks uncertainty variable vector visible weights
Popular passages
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.