Data Fusion is the process of combining multiple data in order
to produce information of tactical value to the user. Data can
come from one or many sources. Sources may be similar, such as
radars, or dissimilar, such as electro-optic, acoustic, or passive
electronic emissions measurement. A key issue is the ability
to deal with conflicting data, producing interim results that
the algorithm can revise as more data becomes available.
Daniel H. Wagner Associates was one of the earliest developers
of Kalman Filter Trackers and Multi-Hypothesis correlators. We
are still at the forefront of fusion technology by combining
Non-Gaussian (or "probability map") target modeling
with standard Kalman Filters in the same algorithm. This is especially
useful for tracking targets with low probabilities of detection
and complicated motion. Modeling non-Gaussian target motion is
particularly critical with land targets as we demonstrated in GADFOS.
