  In this paper, we propose an algorithm for non-myopic sensor management for
Bernoulli filtering, i.e., when there may be at most one target present in the
scene. The algorithm is based on selecting the action that solves a
Bellman-type minimisation problem, whose cost function is the mean square
generalised optimal sub-pattern assignment (GOSPA) error, over a future time
window. We also propose an implementation of the sensor management algorithm
based on an upper bound of the mean square GOSPA error and a Gaussian
single-target posterior. Finally, we develop a Monte Carlo tree search
algorithm to find an approximate optimal action within a given computational
budget. The benefits of the proposed approach are demonstrated via simulations.
