## 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 16

The lower curve gives the detection probability for a single radar sensor as a

function of signal - to - noise

. The detection probability of 0 . 7 is adequate when the signal - to - noise

...

The lower curve gives the detection probability for a single radar sensor as a

function of signal - to - noise

**ratio**when the false alarm probability is equal to 106. The detection probability of 0 . 7 is adequate when the signal - to - noise

**ratio**is...

Page 62

The likelihood

1 , P ( d ; \ H . ) ( 3 - 3 ) where N ... the joint probability distribution of the likelihood

The likelihood

**ratio**is thus : 20 , 29 M ( d ) = - N P ( d ; \ H ; ) , · for i = 1 , 2 , . . . , 9 -1 , P ( d ; \ H . ) ( 3 - 3 ) where N ... the joint probability distribution of the likelihood

**ratios**for each hypothesis as N [ P ( A1 , A2 , . . . , 19 - 1 \ H ; ) j = 1 sen for i = 1 ...Page 130

2 Bayes ' rule in terms of odds probability and likelihood

the interpretation of Bayes ' rule is gained when Eq . ( 5 - 10 ) is divided by P ( H ;

E ) , where H ; represents the negation of Hi . Thus , P ( H ; E ) PĀ ; \ E ) P ( E | H ...

2 Bayes ' rule in terms of odds probability and likelihood

**ratio**Further insight intothe interpretation of Bayes ' rule is gained when Eq . ( 5 - 10 ) is divided by P ( H ;

E ) , where H ; represents the negation of Hi . Thus , P ( H ; E ) PĀ ; \ E ) P ( E | H ...

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### Contents

Multiple Sensor System Applications Benefits and Design | 7 |

Chapter 3 | 51 |

References | 97 |

Copyright | |

9 other sections not shown

### 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 detection probability direction distribution 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 production 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 vector visible weights