Cooperative Trajectory Prediction of UAVs via Generative Adversarial Networks

This paper addresses the cooperative trajectory prediction problem of multiple UAVs in air combat. By using the historical trajectory information of each UAV and its neighbors, a novel GAN-CI algorithm is designed based on generate adversarial networks. The algorithm is used to generate multiple prediction trajectories through a generative adversarial network, and the input information is analyzed and processed by a cooperative information interaction module. It is shown that the algorithm improves the average prediction accuracy by 45% over the generative adversarial network without cooperative interaction information and 38% over the Long Short-Term Memory algorithm with pooling module.