Aerial Group Target Tracking Method Based on Spatial-Temporal Attention Graph Network

Tracking multiple aerial targets within a group is a challenging task in the radar field. Group targets are usually closely spaced and have similar features, which leads to uncertain associations in target tracking. Additionally, targets within a group interact with each other, making it difficult to establish an accurate prior motion model. These factors limit the conventional model-driven tracking methods, resulting in false associations and tracking errors. Therefore, this paper proposes a group target tracking method based on spatial-temporal attention graph networks. First, a generalized graph representation is constructed to model the various interactions among group targets. Then, a two-stage network framework is introduced to learn the spatial and temporal relationships, thereby achieving robust tracking. Specifically, a message passing network with spatial-temporal attention is designed for data association of closely spaced targets, and a graph-aided filter state modification network is designed to correct the tracking error caused by mismatched motion models. Within the framework of multiple hypothesis tracking, data association and track filtering are optimized in a data-driven way. Simulation and experiment results demonstrate that the method outperforms the competing methods.