Probabilistic and Statistical Methods - Information Fusion in Signal and Image Processing - page 65

Chapter 6
Probabilistic and Statistical Methods
6.1. Introduction and general concepts
Probabilistic methods essentially deal with the uncertainty of information. They
rely on solid and well-mastered mathematical theories in signal and image processing,
such as Bayesian decision theory, estimation theory, entropy measurements, etc., thus
making it one of the preferred tools for fusion.
Information and its imperfections (mostly those whose nature can be expressed in
terms of uncertainty) are modeled using probability distributions or statistical mea-
surements. We will see in section 6.2 how this formalism can be used to measure
information. We will then describe the different stages of the fusion process: model-
ing and estimation in section 6.3, Bayesian combination in section 6.4, then Bayesian
combination seen as an estimation problem in section 6.5. The most common rules
of decision making are presented in sections 6.6 and 6.7. The following sections give
examples of applications and other theoretical tools are discussed, in the fields of
multi-source classification in image processing in section 6.8, then of target motion
analysis in signal processing in section 6.9.
6.2. Information measurements
If we have a set of l sources of information I j , a first task often consists of trans-
forming it into a smaller and therefore easier to process subset, without losing any
information.