  This paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter on the
space of sets of tree trajectories for multiple target tracking with spawning
targets. A tree trajectory contains all trajectory information of a target and
its descendants, which appear due to the spawning process. Each tree contains a
set of branches, where each branch has trajectory information of a target or
one of the descendants and its genealogy. For the standard dynamic and
measurement models with multi-Bernoulli spawning, the posterior is a PMBM
density, with each Bernoulli having information on a potential tree trajectory.
To enable a computationally efficient implementation, we derive an approximate
PMBM filter in which each Bernoulli tree trajectory has multi-Bernoulli
branches, obtained by minimising the Kullback-Leibler divergence. The resulting
filter improves tracking performance of state-of-the-art algorithms in a
simulated scenario.
