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 110
... computed by doubling P ( Z≥ z ) because the standard normal distribution is symmetric . The following double - sided test example illustrates how the P - value is used to evaluate the truth of a hypothesis . Suppose the mean thickness ...
... computed by doubling P ( Z≥ z ) because the standard normal distribution is symmetric . The following double - sided test example illustrates how the P - value is used to evaluate the truth of a hypothesis . Suppose the mean thickness ...
Page 140
... computed by requiring Eq . ( 5-34 ) to sum to unity over i . When each H , is conditionally independent of the other hypotheses , Eq . ( 5-28 ) can be applied to give P ( H¡ | E1 , ... , E , ) = a P ( H1 ) [ [ [ P ( E , \ H ; ) ] . j ...
... computed by requiring Eq . ( 5-34 ) to sum to unity over i . When each H , is conditionally independent of the other hypotheses , Eq . ( 5-28 ) can be applied to give P ( H¡ | E1 , ... , E , ) = a P ( H1 ) [ [ [ P ( E , \ H ; ) ] . j ...
Page 163
... computed and combined with the other conditional probabilities using the equation for the posterior probability . In the Dempster- Shafer method , support probabilities for all possible disjunction propositions are computed , making the ...
... computed and combined with the other conditional probabilities using the equation for the posterior probability . In the Dempster- Shafer method , support probabilities for all possible disjunction propositions are computed , making the ...
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