Graph-Based Multiobject Tracking with Embedded Particle Flow
This work addresses the need for efficient multiobject tracking in radar systems, representing an incremental improvement by combining graph-based methods with particle flow for enhanced performance.
The paper tackles the problem of multiobject tracking in nonlinear, high-dimensional scenarios by presenting a graph-based Bayesian method that embeds particle flow, resulting in reduced computational complexity and memory requirements while achieving favorable detection and estimation accuracy in a challenging 3-D simulation.
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.