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 16
... ratio is nominal at 16 dB . But when the target signature is reduced and the signal - to - noise ratio decreases to 10 dB , the detection probability falls to 0.27 , generally not acceptable for radar sensor performance . If , however ...
... ratio is nominal at 16 dB . But when the target signature is reduced and the signal - to - noise ratio decreases to 10 dB , the detection probability falls to 0.27 , generally not acceptable for radar sensor performance . If , however ...
Page 62
... ratio A as the product of terms formed by the conditional probability of a 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 ...
... ratio A as the product of terms formed by the conditional probability of a 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 ...
Page 130
... ratio Further insight into the interpretation of Bayes ' rule is gained when Eq . ( 5-10 ) is divided by P ( H¡ | E ) , where H¡ represents the negation of H¡ . Thus , = = = P ( HE ) P ( E | H ; ) P ( H ; ) P ( E | H1 ) P ( H ; ) _ P ...
... ratio Further insight into the interpretation of Bayes ' rule is gained when Eq . ( 5-10 ) is divided by P ( H¡ | E ) , where H¡ represents the negation of H¡ . Thus , = = = P ( HE ) P ( E | H ; ) P ( H ; ) P ( E | H1 ) P ( H ; ) _ P ...
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
a₁ absorption Adaline algorithm angle measurements angle tracks application artificial neural networks atmospheric B₁ Bayesian inference calculated classical inference classification coefficient combined computed conditional probability confidence interval confidence levels data association data fusion decision Dempster-Shafer Dempster's rule detection modes direction angle distribution elements emitters error estimation evidence example false alarm probability frequency fusion algorithms fusion architecture fusion process fuzzy logic fuzzy sets H₁ hypothesis identification IEEE infrared Kalman filter layer likelihood likelihood function linear LOWTRAN membership functions MODTRAN multibeam antenna multiple sensor multisensor neuron node object observed operating optimal output P-value parameters passive percent perceptron pignistic probability mass Proc processor produced production rules propositions radiometer rain resolution sensor data sensor detection Sensor Fusion sensor system signal processing signatures standard deviation statistical surveillance radar Table target techniques temperature threshold transferable belief model unsupervised learning update vector wavelength weights