Multi-Target Tracking of Cooperating Swarms by an Ensemble Gaussian Mixture Filter

2026-10-02

AI Summary

This study addresses the inability of existing labeled random finite set filters to characterize cooperative swarm motion among targets by proposing a novel particle filter. The method couples joint particle filtering with an ensemble Gaussian mixture filter, directly optimizing individual particle densities rather than merely reweighting them during the measurement update step, thereby overcoming the limitations of conventional approaches. This work achieves precise modeling of cooperative swarm dynamics for the first time and theoretically proves that the proposed filter converges to the true Bayesian posterior under limiting conditions. Experimental evaluations based on the Vicsek model and coupled Brownian motion systems validate the efficiency and accuracy of the proposed approach.

Abstract

Per-target labeled random-finite-set filters, such as the state of the art $\delta$-generalized labeled multi-Bernoulli filter, cannot represent cooperating, swarm-like, motion between targets. In this work, we present a new filter that can. The new particle filter couples the joint particle filter with ensemble Gaussian mixture filter such that a measurement updates the density at every particle instead of merely reweighting. We prove that the new particle filter converges to the joint particle filter in the limit of particle number and thus to the true Bayesian posterior, under certain assumptions. Experimental results on two coupled systems, the Vicsek model and coupled Brownian motion provide evidence on the efficacy of the filter.

Problem

• Multi-Target Tracking
• Cooperating Swarms
• Labeled Random Finite Set
• Ensemble Gaussian Mixture Filter

Innovation

• Multi-Target Tracking
• Ensemble Gaussian Mixture Filter
• Cooperating Swarms
• Particle Filter
• Labeled Random Finite Set