Target Intention Prediction of Air Combat Based on Mog-GRU-D Network Under Incomplete Information
Jun Chen, Xiang Sun, Zhe Xue, Xinyu Zhang · Journal of Systems Engineering and Electronics · 2025
High complexity and uncertainty of air combat pose significant challenges to target intention prediction. Current interpolation methods for data preprocessing and wrangling have limitations in capturing interrelationships among intricate variable patterns. Accordingly, this study proposes a Mogrifier gate recurrent unit-D (Mog-GRU-D) model to address the combat target intention prediction issue under the incomplete information condition. The proposed model directly processes missing data while reducing the independence between inputs and output states. A total of 1 200 samples from twelve continuous moments are captured through the combat simulation system, each of which consists of seven dimensional features. To benchmark the experiment, a missing valued dataset has been generated by randomly removing 20% of the original data. Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25% when dealing with incomplete information. This study provides possible interpretations for the principle of target interactive mechanism, highlighting the model's effectiveness in potential air warfare implementation.