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 268
... emitter environments with relatively low SNRs , the cross - correlation statistic provides a high probability of correctly deciding if a signal is present for a given , but low value of the probability of falsely deciding that an emitter ...
... emitter environments with relatively low SNRs , the cross - correlation statistic provides a high probability of correctly deciding if a signal is present for a given , but low value of the probability of falsely deciding that an emitter ...
Page 272
... emitter locations , to sort through since the true position of the emitters is unknown . Not all M - tuple combinations represent real locations for the emitters . For example , there are M - tuples that will place multiple emitters at ...
... emitter locations , to sort through since the true position of the emitters is unknown . Not all M - tuple combinations represent real locations for the emitters . For example , there are M - tuples that will place multiple emitters at ...
Page 287
... emitter in the sequence of planes produced by one sensor , with an emitter having the closest hinge angle in the sequence produced by another sensor . Once the hinge angles from the two sensors are associated , the range to the emitter ...
... emitter in the sequence of planes produced by one sensor , with an emitter having the closest hinge angle in the sequence produced by another sensor . Once the hinge angles from the two sensors are associated , the range to the emitter ...
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