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.
