  For the standard point target model with Poisson birth process, the Poisson
Multi-Bernoulli Mixture (PMBM) is a conjugate multi-target density. The PMBM
filter for sets of targets has been shown to have state-of-the-art performance
and a structure similar to the Multiple Hypothesis Tracker (MHT). In this paper
we consider a recently developed formulation of multiple target tracking as a
random finite set (RFS) of trajectories, and present three important and
interesting results. First, we show that, for the standard point target model,
the PMBM density is conjugate also for sets of trajectories. Second, based on
this we develop PMBM trackers (trajectory RFS filters) that efficiently
estimate the set of trajectories. Third, we establish that for the standard
point target model the multi-trajectory density is PMBM for trajectories in any
time window, given measurements in any (possibly non-overlapping) time window.
In addition, the PMBM trackers are evaluated in a simulation study, and shown
to yield state-of-the-art performance.
