  Target tracking represents a state estimation problem recurrent in many
practical scenarios like air traffic control, autonomous vehicles, marine radar
surveillance and so on. In a Bayesian perspective, when phenomena like clutter
are present, the vast majority of the existing tracking algorithms have to deal
with association hypotheses which can grow in the number over time; in that
case, the posterior state distribution can become computationally intractable
and approximations have to be introduced. In this work, the impact of the
number of hypotheses and corresponding reductions is investigated both in terms
of employed computational resources and tracking performances. For this
purpose, a recently developed adaptive mixture model reduction algorithm is
considered in order to assess its performances when applied to the problem of
single object tracking in the presence of clutter and to provide additional
insights on the addressed problem.
