In the unmanned aerial vehicles (UAVs) flight control and navigation guidance system, trajectory prediction serves as a critical foundational component, with its accuracy and reliability directly influencing the system performance of the UAVs. However, existing research has predominantly focused on optimizing algorithm efficiency, failing to fully consider the impact of the UAV’s flight status on its trajectory. This has resulted in significant discrepancies between predicted results and actual trajectories in complex scenarios. Therefore, this paper proposes a trajectory prediction algorithm that integrates the UAVs’ behavioral intentions. Firstly, a behavioral intention recognition model is constructed using the Support Vector Machine (SVM) to accurately discriminate the UAV’s motion patterns and output the probability distribution of its future actions, thereby integrating semantic-level intention information into the prediction process. Secondly, the Bidirectional Gated Recurrent Unit (Bi-GRU) is employed to mine the spatial-temporal correlation features from trajectory data. Additionally, an attention mechanism is introduced to capture key information of sequence, enhancing the model’s ability to represent complex motion trends. The results of simulation experiments demonstrate that this algorithm exhibits significant advantages in terms of trajectory prediction accuracy and scene adaptability, providing more practical technical support for intelligent navigation and safety control of UAVs.
Cao et al. (2025) studied this question.