  This paper proposes an efficient implementation of the Poisson
multi-Bernoulli mixture (PMBM) trajectory filter. The proposed implementation
performs track-oriented N-scan pruning to limit complexity, and uses dual
decomposition to solve the involved multi-frame assignment problem. In contrast
to the existing PMBM filter for sets of targets, the PMBM trajectory filter is
based on sets of trajectories which ensures that track continuity is formally
maintained. The resulting filter is an efficient and scalable approximation to
a Bayes optimal multi-target tracking algorithm, and its performance is
compared, in a simulation study, to the PMBM target filter, and the delta
generalized labelled multi-Bernoulli filter, in terms of state/trajectory
estimation error and computational time.
