  The Integrated Probabilistic Data Association Filter (IPDAF) is a target
tracking algorithm based on the Probabilistic Data Association Filter that
calculates a statistical measure that indicates if an estimated representation
of the target properly represents the target or is generated from
non-target-originated measurements. The main contribution of this paper is to
adapt the IPDAF to constant velocity target models that evolve on connected,
unimodular Lie groups, and where the measurements are also defined on a Lie
group. We present an example where the methods developed in the paper are
applied to the problem of tracking a ground vehicle on the special Euclidean
group SE(2).
