  This paper proposes a flight controller for an unmanned aerial vehicle (UAV)
to loiter over a ground moving target (GMT). We are concerned with the scenario
that the stochastically time-varying maneuver of the GMT is unknown to the UAV,
which renders it challenging to estimate the GMT's motion state. Assuming that
the state of the GMT is available, we first design a discrete-time Lyapunov
vector field for the loitering guidance and then design a discrete-time
integral sliding mode control (ISMC) to track the guidance commands. By
modeling the maneuver process as a finite-state Markov chain, we propose a
Rao-Blackwellised particle filter (RBPF), which only requires a few number of
particles, to simultaneously estimate the motion state and the maneuver of the
GMT with a camera or radar sensor. Then, we apply the principle of certainty
equivalence to the ISMC and obtain the flight controller for completing the
loitering task. Finally, the effectiveness and advantages of our controller are
validated via simulations.
