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 61
... probability mass associated with a decision . The probability mass assignments are conditioned on each postulated hypothesis either through Bayesian reasoning or belief functions as in Dempster - Shafer theory . In the Bayesian approach ...
... probability mass associated with a decision . The probability mass assignments are conditioned on each postulated hypothesis either through Bayesian reasoning or belief functions as in Dempster - Shafer theory . In the Bayesian approach ...
Page 156
... probability masses provided by multiple sensors or knowledge sources for compatible propositions . The intersection of the propositions having the largest probability mass is selected as the output of the fusion process . Propositions ...
... probability masses provided by multiple sensors or knowledge sources for compatible propositions . The intersection of the propositions having the largest probability mass is selected as the output of the fusion process . Propositions ...
Page 158
... probability mass associated with this element is m ( a1 a2 ) = m1 ( a1 ○ a ̧ ) m2 ( ☺ ) = ( 0.6 ) ( 0.3 ) = 0.18 ( 6-17 ) and corresponds to the proposition that the target is type 1 , either friendly or hostile . The proposition ...
... probability mass associated with this element is m ( a1 a2 ) = m1 ( a1 ○ a ̧ ) m2 ( ☺ ) = ( 0.6 ) ( 0.3 ) = 0.18 ( 6-17 ) and corresponds to the proposition that the target is type 1 , either friendly or hostile . The proposition ...
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 tracks application artificial neural networks atmospheric attenuation B₁ Bayesian inference calculated classical inference classification coefficient coherent processor combined computed conditional probability confidence interval confidence levels correlation data association data fusion decision Dempster-Shafer Dempster's rule detection modes direction angle measurements distribution elements emission emitters error estimation example false alarm probability frequency fusion algorithms fusion architecture fusion process fuzzy logic fuzzy sets H₁ hypothesis identification infrared Kalman filter layer likelihood function linear membership functions MODTRAN multibeam antenna multiple sensor multisensor neuron node object operating optimal output P-value parameters passive percent perceptron probability mass Proc produced production rules proposition radiometer rain range ratio resolution scans sensor data Sensor Fusion sensor system signal processing signatures standard deviation statistical surveillance radar Table techniques temperature threshold track from Sensor unsupervised learning update vector voting logic wavelength weights