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 76
... method of association shown is generally appropriate for the given tracking complexity . Of course , the more ... method , used with track splitting , augments the state of the parent track to include the maneuver . The second method ...
... method of association shown is generally appropriate for the given tracking complexity . Of course , the more ... method , used with track splitting , augments the state of the parent track to include the maneuver . The second method ...
Page 136
... method to combine identity declarations from multiple sensors to obtain a new and hopefully improved joint identity declaration . Required inputs for the Bayes method are the ability to compute or model P ( E | H ; ) , i.e. , P ( D | O ) ...
... method to combine identity declarations from multiple sensors to obtain a new and hopefully improved joint identity declaration . Required inputs for the Bayes method are the ability to compute or model P ( E | H ; ) , i.e. , P ( D | O ) ...
Page 265
... methods are discussed to solve the maximum likelihood problem . In the first method , the maximum likelihood process is ... method , the computational requirements are reduced by applying a relaxation algorithm to solve the maximum ...
... methods are discussed to solve the maximum likelihood problem . In the first method , the maximum likelihood process is ... method , the computational requirements are reduced by applying a relaxation algorithm to solve the maximum ...
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