## 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 102

In this case , the

where o is the

size . The

deviation ...

In this case , the

**standard**deviation of the sample mean is 0 = o / vn , ( 4 - 1 )where o is the

**standard**deviation of the entire population and n is the samplesize . The

**standard**deviation of the sample mean is smaller than the**standard**deviation ...

Page 103

The sample mean ő is 461 and the

100 / 500 = 4 . 5 . Therefore , we can state that we are 95 percent confident that

the unknown mean score for the 250 , 000 students lies between ž - 9 = 461 – 9 ...

The sample mean ő is 461 and the

**standard**deviation of the sample mean Or is100 / 500 = 4 . 5 . Therefore , we can state that we are 95 percent confident that

the unknown mean score for the 250 , 000 students lies between ž - 9 = 461 – 9 ...

Page 110

The z statistic has a

is true . If the alternative hypothesis is one sided on the high side , i . e . , H : u >

Mo , then the P - value is the probability that a

...

The z statistic has a

**standard**normal distribution N ( uo , olvn ) when Ho : u = Hois true . If the alternative hypothesis is one sided on the high side , i . e . , H : u >

Mo , then the P - value is the probability that a

**standard**normal random variable Z...

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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