An Adaptive Multivariate Approach to Dynamic Group Target Tracking Using Variational Inference

The robust tracking of group targets, such as bird flocks and UAV swarms, is crucial for situational awareness and individual tracking. The main characteristics of group targets are their complex spatial structures and highly dynamic behavior. Traditional single ellipsoidal tracking methods cannot accurately describe such complex shapes. On the other hand, existing multi-ellipsoidal tracking methods assume mutual independence between sub-objects. This assumption can lead to inaccurate responses to changes in group states and false associations between measurements and sub-objects. This paper proposes an adaptive multivariate approach for dynamic group target tracking. A multivariate model is first proposed to model sub-object states precisely. Both collective and internal structural relations are incorporated to adapt to the group's complicated dynamics. A variational Bayesian approach is derived to jointly estimate the multivariate state and the association weights of measurements. Additionally, an adaptive sub-object partitioning algorithm is proposed to analyze various group structures automatically. The effectiveness of the proposed method is verified by simulation and experiment results. The results demonstrate that our method outperforms state-of-the-art methods in tracking accuracy and robustness, especially in dynamic scenarios.