MVW-GNN: a multiview graph representation learning method for multisource track association
Li Yuan, Yang Zhang, Kai Liu, Yuchen Zhao, Kaibo Zhou · Measurement Science and Technology · 2025
Abstract In a dynamic threat environment, multisource track association is a major technical challenge for achieving large-scale real-time target tracking. However, existing methods focus mostly on matching low-dimensional kinematic features, hindering the capture of deep semantic coupling between different modal data, resulting in superficial association results and limited insight into complex behaviors. To overcome this limitation, we propose a multisource track association method based on a multiview weighted graph neural network, which aims to improve the association accuracy from the perspectives of multigranularity graph semantic modeling and robust deep feature learning. First, to mitigate the impact of noise present in the track data, we employ variational mode decomposition to smooth the data and reduce association-induced errors. We subsequently design a view distinguishing strategy that determines edge weight thresholds on the basis of weighted Euclidean distances. Specifically, if the distance between two nodes exceeds the threshold, a first-order graph is constructed to capture global physical differences; otherwise, a second-order graph is built to capture local similarities and reveal potential strong associations. Furthermore, we design a residual-aware attention graph convolutional network (GCN), in which residual connections are embedded into the GCN architecture to suppress the effect of oversmoothing. Finally, we construct a structural invariance constraint to facilitate the effective learning of subgraphs under structural inconsistencies. The experimental results demonstrate that, under the same testing conditions, the proposed method outperforms other comparative algorithms by effectively extracting high-dimensional features from tracks, with an average F1 score improvement of 12.5%, which has broad research prospects and application value.