Maneuvering Target Tracking Using GAT Autoencoder with Clutters

This paper considers the problem of maneuvering target tracking in the clutter environment and proposes a graph attention autoencoder tracking network. Initially, the proposed method constructs the graph by using historical track points and measurement data of the current frame, including the target measurement and clutters, as vertices. Then, the graph is fed into the designed graph attention autoencoder (GAE) network to learn the relationships among the historical trajectory and the measurements of the current frame. Finally, the GAE outputs the tracking result of the current frame. The experimental results highlight that the proposed GAE can outperform the classic maneuvering target tracking algorithms in the clutter environment with low and high maneuverability.