Fusion in Signal Processing - Information Fusion in Signal and Image Processing - page 14

The major concepts are directly related to information processing. Data fusion sys-
tems rely mostly on a series of modeling, estimation, retiming and data association,
combination (or fusion itself) of elements of information, and then decision making or
supervision steps. Going from the knowledge of a bit of information to a mathematical
representation that renders it usable constitutes the information modeling stage. The
retiming and data association phase is preliminary to the combination or fusion phase
of multi-source information. The first three phases are usually clearly uncorrelated
from the decision making phase, which consists of expressing compromise problems
(costs, risks, etc.). These concepts allow us to achieve improvements due to the com-
plementarity and redundancy of the pre-existing information and of the measurements.
A system's efficiency then results from the complexity of the resulting system, from
the reliability of the model, from the retiming and association techniques, from the
clever combination of the information, and finally from the decisions that are made.
At the same time, information and communications systems are expected to assist
and co-operate with the operators of the application field (the users) with the goal of
reaching a decision. There are functions that are entirely automated on a local scale
over which the operator has no element of control because these functions are reliable
and/or accurate enough. On the other hand, the system as a whole has to be interactive
with the user, who has to be able to control certain parts of the system by modifying,
for example, confidence levels on whether a set of considered hypotheses is complete,
or by defining in real-time a balance between different decision criteria. The system
should also be capable of providing complementary information, upon request from
the user, for example, on the level of conflict between elements of information.
One of the fundamental ideas has to do with the meaning of information and the
combination mechanisms in a broad sense. The modeling that is chosen has to be
suited accurately to the meaning of the information that is actually available. This
accuracy in modeling causes problems of heterogenity or hybridism in the representa-
tion of data. This leads to the suggestion of modeling and heterogenous fusion mecha-
nisms where the concept of reliability between the meaning of the information actually
available, and the meaning of the mathematical representation is essential.
The question of focusing more on the combination mechanisms rather than the
semantics, or vice versa, divides researchers in this field. In the field of signal process-
ing, the trend among authors has been to emphasize mechanisms based on the idea
that the process's quality essentially relies on the quality of the mechanisms involved.
Probability theory, based on a strong sense of modeling, gives us well-known and most
importantly well-controlled mechanisms (simulated annealing, hypothesis test, multi-
model Kalman filtering, etc.). From this perspective, probability theory is therefore
the “right” theoretical framework which has been particularly well studied by a num-
ber of researchers, despite certain drawbacks regarding the reliability of the seman-
tic representations when there is little information, but the semantic aspect remains