Fusion Techniques for Reliable Information: A Survey <s> Probabilistic Theory (PrT) <s> Data fusion or information fusion are names which have been primarily assigned to military-oriented problems. In military applications, typical data fusion problems are: multisensor, multitarget detection, object identification, tracking, threat assessment, mission assessment and mission planning, among many others. However, it is clear that the basic underlying concepts underlying such fusion procedures can often be used in nonmilitary applications as well. The purpose of this book is twofold: First, to point out present gaps in the way data fusion problems are conceptually treated. Second, to address this issue by exhibiting mathematical tools which treat combination of evidence in the presence of uncertainty in a more systematic and comprehensive way. These techniques are based essentially on two novel ideas relating to probability theory: the newly developed fields of random set theory and conditional and relational event algebra. This volume is intended to be both an update on research progress on data fusion and an introduction to potentially powerful new techniques: fuzzy logic, random set theory, and conditional and relational event algebra. Audience: This volume can be used as a reference book for researchers and practitioners in data fusion or expert systems theory, or for graduate students as text for a research seminar or graduate level course. <s> BIB001 </s> Fusion Techniques for Reliable Information: A Survey <s> Probabilistic Theory (PrT) <s> Information fusion introduces special operators o in probability theory and fuzzy theory. Some serious data certify in each case these two quite distinct techniques. The article shows that four postulates are the unique aim of these two theories. Evidence theory and fuzzy set theory often replace probabilities in medicine, economy and control. Fuzzy theory is used for example in a Japanese photographic engine. We solved the challenge of unifying such different techniques. With the four postulates: noncontradiction, continuity, universality, context dependence, we obtain the same functional equation from which are deduced probability and fuzzy set theories. The same postulates apply to confidences either in the dependence or independence situation. The foundation for the various modern theories of information fusion has been unified in the framework of uncertainty by deductions. The independence between elementary confidences do not need to be understood in the sense of probabilistic meaning. <s> BIB002
Random variables, stochastic processes, and related events are combined to analyze random phenomena. A mathematical abstraction of non-deterministic event or quantities may either be simply occurrences or evolves over time in an apparently random fashion BIB001 . The sequence of the random events shows certain statistical patterns after repeating many times. The system studies and predicts the context or the situation. To resolve uncertain reasoning in human activities, this model is used with "Possible Theory (PoT)"  or "Fuzzy Set Theory (FST)" BIB002 . In pervasive computing areas, it seems well adapted to problems involving independent random variables, which are essentially arisen at low levels of the fusion process.