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 61
... 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 test . The ...
... 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 test . The ...
Page 144
... equal to the preprocessed signal amplitude , and PMD ( data | O¡ ) is equal to the probability of receiving a signal of some amplitude given the object is of type O1 . These probabilities are found from extensive experiments with buried ...
... equal to the preprocessed signal amplitude , and PMD ( data | O¡ ) is equal to the probability of receiving a signal of some amplitude given the object is of type O1 . These probabilities are found from extensive experiments with buried ...
Page 153
... equal to unity , as is the uncertainty interval . The uncertainty interval denoted by [ 0.6 , 0.6 ] contains equal support and plausibility values . It indicates a definite probability of 0.6 for proposition a , since both the direct ...
... equal to unity , as is the uncertainty interval . The uncertainty interval denoted by [ 0.6 , 0.6 ] contains equal support and plausibility values . It indicates a definite probability of 0.6 for proposition a , since both the direct ...
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