  This paper presents a solution for recovering full trajectory information,
via the calculation of the posterior of the set of trajectories, from a
sequence of multitarget (unlabelled) filtering densities and the multitarget
dynamic model. Importantly, the proposed solution opens an avenue of trajectory
estimation possibilities for multitarget filters that do not explicitly
estimate trajectories. In this paper, we first derive a general multitrajectory
forward-backward smoothing equation based on sets of trajectories and the
random finite set framework. Then we show how to sample sets of trajectories
using backward simulation when the multitarget filtering densities are
multi-Bernoulli processes. The proposed approach is demonstrated in a
simulation study.
