Multi-target Situational Swareness of Naval War Based on ResNet-ViT

Battlefield situation intent consists of a series of tactical motivations, which are time series, dynamic and multi-objective. However, the existing situational awareness methods have the problem of only studying single objectives or ignoring time series. Based on the characteristics of the multiple-objective and long period in the background of naval warfare, a comprehensive situational awareness model of naval warfare based on the ResNet-ViT network is presented. The residual neural network (ResNet) extracts spatial features between targets, whereas the vision transformer (ViT) network captures time-series features by using the transformer's ability to mine long-distance dependency features. The experimental results show that the proposed model can predict the intent of naval warfare with 92%~95% accuracy (proportion of correctly predicted samples to total samples), which solves the intention prediction problem of multi-target cooperative warfare over a long period.