Spatiotemporal model for real-time projectile prediction in digital prototyping of artillery

Abstract

To address the growing demand for rapid and precise prediction of projectile trajectories in modern digital artillery systems, this paper presents an innovative spatiotemporal prediction model based on deep learning. Unlike traditional methods that rely on simplified assumptions and step-by-step numerical integration, the proposed model explicitly learns spatial relationships and temporal evolution of projectile motion through a combination of a Spatial Transformer Encoder, a Temporal GRU Encoder, and a Temporal GRU Decoder. A graph-based attention mechanism and nonlinear fusion in the latent space further enhance the model’s ability to capture complex physical interactions influenced by environmental factors such as wind speed and firing angle. The model is initially trained using extensive high-fidelity simulation data and then fine-tuned with a small amount of real firing data, which improves its adaptability to real-world conditions and reduces the simulation-to-reality bias. Experimental results show that the model accurately predicts key ballistic parameters under varying conditions while achieving real-time inference performance suitable for engineering deployment. Its lightweight structure and strong compatibility facilitate easy integration with platforms such as Unreal Engine for virtual testing, tactical training, and intelligent weapon system development. Comparative evaluations and ablation studies verify that the proposed spatiotemporal prediction approach consistently outperforms existing methods in both accuracy and efficiency.