Unifying Tree-Reweighted Belief Propagation and Mean Field for Tracking Extended Targets

TL;DR

This paper introduces a unified belief propagation and mean field approach for scalable extended target tracking, improving convergence and efficiency over existing particle-based methods while avoiding measurement clustering.

Contribution

It unifies tree-reweighted BP and MF in a factor graph framework, providing a closed-form recursive solution for extended target tracking with better convergence and efficiency.

Findings

Enhanced tracking performance demonstrated in simulations

More efficient than particle-based BP algorithms

Avoids measurement clustering and gating

Abstract

This paper proposes a unified tree-reweighted belief propagation (BP) and mean field (MF) approach for scalable detection and tracking of extended targets within the framework of factor graph. The factor graph is partitioned into a BP region and an MF region so that the messages in each region are updated according to the corresponding region rules. The BP region exploits the tree-reweighted BP, which offers improved convergence than the standard BP for graphs with massive cycles, to resolve data association. The MF region approximates the posterior densities of the measurement rate, kinematic state and extent. For linear Gaussian target models and gamma Gaussian inverse Wishart distributed state density, the unified approach provides a closed-form recursion for the state density. Hence, the proposed algorithm is more efficient than particle-based BP algorithms for extended target tracking. This method also avoids measurement clustering and gating since it solves the data association problem in a probabilistic fashion. We compare the proposed approach with algorithms such as the Poisson multi-Bernoulli mixture filter and the BP-based Poisson multi-Bernoulli filter. Simulation results demonstrate that the proposed algorithm achieves enhanced tracking performance.