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
... standard deviation of the sample mean is σ1 = o / √n , ( 4-1 ) where σ is the standard deviation of the entire population and n is the sample size . The standard deviation of the sample mean is smaller than the standard deviation of ...
... standard deviation of the sample mean is σ1 = o / √n , ( 4-1 ) where σ is the standard deviation of the entire population and n is the sample size . The standard deviation of the sample mean is smaller than the standard deviation of ...
Page 103
... standard deviation of the sample mean σ , is 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 452 and x + 9 = 461 + 9 = 470 . - = 9 461 – 9 ...
... standard deviation of the sample mean σ , is 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 452 and x + 9 = 461 + 9 = 470 . - = 9 461 – 9 ...
Page 110
... standard deviation o . The z statistic has a standard normal distribution N ( uo , σ / √n ) when Ho : μ μo is true . Ho = If the alternative hypothesis is one sided on the high side , i.e. , H1 : μ > μo , then the P - value is the ...
... standard deviation o . The z statistic has a standard normal distribution N ( uo , σ / √n ) when Ho : μ μo is true . Ho = If the alternative hypothesis is one sided on the high side , i.e. , H1 : μ > μo , then the P - value is the ...
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