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

Graph-Based Multiobject Tracking with Embedded Particle Flow