  This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for
multi-target tracking: one to estimate the set of alive trajectories at each
time step and another to estimate the set of all trajectories, which includes
alive and dead trajectories, at each time step. The filters are based on
propagating a Poisson multi-Bernoulli (PMB) density on the corresponding set of
trajectories through the filtering recursion. After the update step, the
posterior is a PMB mixture (PMBM) so, in order to obtain a PMB density, a
Kullback-Leibler divergence minimisation on an augmented space is performed.
The developed filters are computationally lighter alternatives to the
trajectory PMBM filters, which provide the closed-form recursion for sets of
trajectories with Poisson birth model, and are shown to outperform previous
multi-target tracking algorithms.
