A multilevel interactive framework based Siamese graph networks for multisource track association

Abstract

Multisource track association (MTA) is a key measurement-fusion problem in maritime situational awareness, where tracks reported by heterogeneous sensors must be correctly matched under asynchronous sampling, sensor noise, and complex target maneuvers. Existing deep learning-based methods have improved association accuracy by learning discriminative track representations. However, many of them encode two tracks separately and compare only pooled track-level embeddings, so the fine-grained interactions between individual track points are introduced only implicitly. This may limit their ability to distinguish targets with similar motion patterns in dense or ambiguous scenarios. To address this issue, this paper proposes a multilevel interactive Siamese graph neural network, termed (MIS-GNN). First, each track is represented as a graph and encoded by a weight-sharing Siamese GraphSAGE encoder to preserve local spatiotemporal topology. Then, a global interaction layer based on cross-track self-attention is introduced to enable node-level message passing between two track graphs, providing a soft alignment mechanism for asynchronous observations. Finally, graph-to-node cross-attention and multiscale graph-to-graph similarity measurements are jointly employed to evaluate track similarity from complementary granularities. Experiments on the public MTAD benchmark demonstrate that MIS-GNN achieves an average F1-score of 0.9264, outperforming state-of-the-art comparison methods. Additional sensitivity, significance, and efficiency analyses further verify the robustness and deployment feasibility of the proposed method.