  Seamless situational awareness provided by modern radar systems relies on
effective methods for multiobject tracking (MOT). This paper presents a
graph-based Bayesian method for nonlinear and high-dimensional MOT problems
that embeds particle flow. To perform operations on the graph effectively,
particles are migrated towards regions of high likelihood based on the solution
of a partial differential equation. This makes it possible to obtain good
object detection and tracking performance with a relatively small number of
particles even if object states are high dimensional and sensor measurements
are very informative. Simulation results demonstrate reduced computational
complexity and memory requirements as well as favorable detection and
estimation accuracy in a challenging 3-D MOT scenario.
