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 18
... increases as the operating frequency increases . In the infrared portion , attenuation is a strong function 18 SENSOR AND DATA FUSION : A TOOL FOR INFORMATION Assessment and DecISION MAKING 2 2 Influence of wavelength on atmospheric ...
... increases as the operating frequency increases . In the infrared portion , attenuation is a strong function 18 SENSOR AND DATA FUSION : A TOOL FOR INFORMATION Assessment and DecISION MAKING 2 2 Influence of wavelength on atmospheric ...
Page 106
... increases as the desired level of confidence increases , dispersion of the sample data increases , and the allowable error decreases . The size of the entire population does not influence the sample size as long as the population is ...
... increases as the desired level of confidence increases , dispersion of the sample data increases , and the allowable error decreases . The size of the entire population does not influence the sample size as long as the population is ...
Page 296
... increases , for example , with length of time one or more features are greater than some threshold , magnitude of received signal , number of features that match predefined target attributes , degree of matching of the features to those ...
... increases , for example , with length of time one or more features are greater than some threshold , magnitude of received signal , number of features that match predefined target attributes , degree of matching of the features to those ...
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