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 158
... elements When the intersection of the propositions that define the inner matrix elements form an empty set , the probability mass of the empty set elements is set equal to zero and the probability mass assigned to the nonempty set ...
... elements When the intersection of the propositions that define the inner matrix elements form an empty set , the probability mass of the empty set elements is set equal to zero and the probability mass assigned to the nonempty set ...
Page 193
... elements . Equation ( 7-7 ) also bounds the statistical capacity of a two - layer signum network . The following ... elements for a feedforward network is problem dependent and often involves considerable engineering judgment . While ...
... elements . Equation ( 7-7 ) also bounds the statistical capacity of a two - layer signum network . The following ... elements for a feedforward network is problem dependent and often involves considerable engineering judgment . While ...
Page 205
... element , from 001 in 2 elements , from 101 in 3 elements , and from 111 in 2 elements . The nearest pattern is 000 , which belongs to 0 set . Therefore , the neuron does not fire when the input is equal to 010 since 000 is a member of ...
... element , from 001 in 2 elements , from 101 in 3 elements , and from 111 in 2 elements . The nearest pattern is 000 , which belongs to 0 set . Therefore , the neuron does not fire when the input is equal to 010 since 000 is a member of ...
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