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. |
From inside the book
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Page 61
... decision j is equal to the conditional probability of the decision given a hypothesis i . Probability mass assignments are optimal in that they minimize total risk . Ho As an example , consider two hypotheses Ho and H1 that are under ...
... decision j is equal to the conditional probability of the decision given a hypothesis i . Probability mass assignments are optimal in that they minimize total risk . Ho As an example , consider two hypotheses Ho and H1 that are under ...
Page 62
... decision given hypothesis H ; divided by the conditional probability of a decision given hypothesis Ho , where the number of terms equals the number of sensors in the fusion system . The likelihood ratio is thus : 26,29 A ( d ) = II N P ...
... decision given hypothesis H ; divided by the conditional probability of a decision given hypothesis Ho , where the number of terms equals the number of sensors in the fusion system . The likelihood ratio is thus : 26,29 A ( d ) = II N P ...
Page 163
... decision space has to be redefined , Dempster - Shafer is simpler to apply than the Bayesian approach . For the latter , changing elements in the decision space requires a completely new derivation of the posterior probabilities for all ...
... decision space has to be redefined , Dempster - Shafer is simpler to apply than the Bayesian approach . For the latter , changing elements in the decision space requires a completely new derivation of the posterior probabilities for all ...
Contents
Multiple Sensor System Applications Benefits and Design | 7 |
Data Fusion Algorithms and Architectures | 51 |
Classical Inference | 101 |
Copyright | |
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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
adaptive algorithm allows angle application approach architecture assigned association atmospheric Bayesian belief calculated Chapter classification coefficient combined computed conditional confidence levels contains corresponding data fusion decision defined Dempster-Shafer described detection probability direction distribution effects elements emitters energy equal error estimation evidence example false alarm probability Figure frequency function fuzzy fuzzy sets given hypothesis identification illustrated inference input interval layer learning likelihood logic mean measurements membership method modes multiple multiple sensor normal object observed operating optimal output parameters passive pattern percent performance population position probability mass problem processing processor produced propositions radar rain range ratio received represents resolution rule sample sample mean selected sensor resolution shown signal signatures single solution sources space standard statistical Table target techniques temperature theory threshold track true update vector visible weights