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Closed-Form Message Passing Algorithms for Tracking Extended Targets
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AI
DOI：10.1109/TAES.2026.3656163.png)
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Abstract
En 中文
For extended target tracking (ETT), factor graph-based algorithms have demonstrated significant advantages over traditional clustering-based approaches. However, one limitation of these methods is that they lack analytical solutions and resort to particle-based implementations. Thus, their performance depends on the number of particles and the proposal distributions. This article proposes two unified message passing algorithms for ETT that provide closed-form solutions within the factor graph framework. These algorithms exploit different formulations of data association and distinct factor graph representations of the ETT problem. The first utilizes the merged belief propagation (BP) and mean field (MF) approach, while the second combines tree-reweighted BP and MF approach. Both partition the factor graphs into MF and BP regions, where the MF region approximates the kinematic state and extent densities, and the BP region resolves data association. In the first algorithm, the BP region is tree-structured, enabling exact computation of association marginals. In contrast, the BP region in the second algorithm comprises numerous loops due to a more detailed data association model, for which tree-reweighted BP is employed to improve message and belief convergences. Due to their analytical implementations, the proposed algorithms are more computationally efficient than particle-based methods. Simulation results demonstrate that the proposed approaches achieve improved tracking performance compared to state-of-the-art algorithms.
Keywords:
Extended target tracking (ETT)
factor graph
free energy
mean field (MF)
tree-reweighted belief propagation (BP)
Journal
IF:
5.7
Papers:
686
Citations:
2.4W