  This paper addresses the problem of real-time detection and tracking of a
non-cooperative target in the challenging scenario with almost no a-priori
information about target birth, death, dynamics and detection probability.
Furthermore, there are false and missing data at unknown yet low rates in the
measurements. The only information given in advance is about the
target-measurement model and the constraint that there is no more than one
target in the scenario. To solve these challenges, we model the movement of the
target by using a trajectory function of time (T-FoT). Data-driven T-FoT
initiation and termination strategies are proposed for identifying the
(re-)appearance and disappearance of the target. During the existence of the
target, real target measurements are distinguished from clutter if the target
indeed exists and is detected, in order to update the T-FoT at each scan for
which we design a least-squares estimator. Simulations using either linear or
nonlinear systems are conducted to demonstrate the effectiveness of our
approach in comparison with the Bayes optimal Bernoulli filters. The results
show that our approach is comparable to the perfectly-modeled filters, even
outperforms them in some cases while requiring much less a-prior information
and computing much faster.
