Sensor and Data Fusion: A Tool for Information Assessment and Decision Making
Annotation 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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Multiple Sensor System Applications Benefits and Design
Data Fusion Algorithms and Architectures
DempsterShafer Evidential Theory
Artificial Neural Networks
Voting Logic Fusion
Fuzzy Logic and Fuzzy Neural Networks
Passive Data Association Techniques for Unambiguous
Appendix A Planck Radiation Law and Radiative Transfer
Appendix B Voting Fusion with Nested Confidence Levels
absorption Adaline adaptive algorithm angle measurements applications artificial neural networks assigned atmospheric attenuation Bayesian inference calculated classical inference classification combined computed conditional probability confidence interval confidence levels data association data fusion decision defined Dempster's rule detection modes direction angle distribution elements emission emitters error estimation evidence example false alarm probability frequency fusion architecture fusion process fuzzy logic fuzzy sets hypothesis identification IEEE infrared input patterns Kalman filter layer likelihood function linear LOWTRAN membership functions MODTRAN multibeam antenna multisensor neuron node object observed operating optimal output parameters passive percent perceptron pignistic probability mass Proc processor production rules propositions radiometer random ratio represents resolution sample mean sensor data sensor detection Sensor Fusion signal processing signatures standard deviation statistical surveillance radar Table target techniques temperature three-sensor threshold transferable belief model uncertainty interval unsupervised learning update variable vector voting logic wavelength weights
Page iv - Data fusion is a multilevel, multifaceted process dealing with the automatic detection, association, correlation, estimation, and combination of data and information from single and multiple sources to achieve refined position and identity estimates, and complete and timely assessments of situations and threats and their significance.