  We consider a moving target and an active pursing agent, modeled as an
intelligent active Brownian particle capable of sensing the instantaneous
target location and adjust its direction of motion accordingly. An analytical
and simulation study in two spatial dimensions reveals that pursuit performance
depends on the interplay between self-propulsion, active reorientation, and
random noise. Noise is found to have two opposing effects: (i) it is necessary
to disturb regular, quasi-elliptical trajectories around the target, and (ii)
slows down pursuit by increasing the traveled distance of the pursuer. We also
propose a strategy to sort active pursuers according to their motility by
circular target trajectories.
