Fusion in Signal Processing - Information Fusion in Signal and Image Processing - page 19

- when the accuracies of the two distributions are close, a dissymmetric fusion
of the type revision or update has to be implemented. This mechanism then makes it
possible to manage some sort of a compromise between the confidence we have in
the prediction mechanism and the confidence we have in the mechanism which, based
on an observation, allows us to go from an estimate to the position. This continuous
adjustment is fundamental if we hope to obtain an almost optimal and hence robust
solution with respect to imprecisions on measurements and models.
When the first windowing mechanism does not authorize the combination, there
is no actual combination. Data that is not assigned to any track is not discarded. It
takes part in a mechanism for creating a new track. We can therefore consider that we
are performing a non-symmetric disjunctive combination at the level of track manage-
ment.
These comments on the estimation and calculation of a law a posteriori show that
fusion in signal processing, even if it is still oriented towards statistical and proba-
bilistic techniques, relies on the same basic physical or logical principles as in other
fields.
2.2.2. Discriminating between several hypotheses and identifying
In a large number of identification problems, we have, on the one hand, infor-
mation characterizing each hypothesis, class or type to recognize and, on the other
hand, information extracted from observations. These two elements of information
are provided for a set of attributes that can be seen as different explanations of the real
situation, and which have to be exploited together. The information characterizing the
classes will be referred to as a priori information, since it specifies what we can expect
for the values of the attributes, conditionally to each hypothesis, before obtaining an
observation. As for the observations (perceptive information), they are measurements
of these attributes. This approach is maintained at every information level. Thus, an
observation can be obtained from a possibly complex, previous process. At any rate,
the imperfection of each observation has to be defined, whether it is a crude, low-level
measurement or a high level perspective of the situation.
We will use indifferently the words sensor, observer or source of information when
referring to any instrument capable of providing information on an object or an attrib-
ute assumed to be part of a continuous or discrete set. The discrete set of hypotheses
within which we will have to discriminate is referred to as the frame of discernment.
In the example of Figure 2.3, we have, as our input, three a priori distributions
involving the speed of a moving object conditionally to the type to which the moving
object belongs (above). The graph in the top-right corner shows the measurement of
the moving object's speed provided by a Doppler radar and the associated imprecision.