Heterogeneous Feature Encoding and Multi-Scale Temporal Fusion for Air Target Intent Recognition
TL;DR
A deep learning framework that learns motion and semantic information separately and combines historical observations from multiple time ranges and achieves higher recognition accuracy and better robustness than representative baseline models is developed.
Abstract
Air target intent recognition plays an important role in situation awareness and decision support in complex air-combat environments. However, existing methods often process motion and semantic information in the same manner, making it difficult to fully exploit their different characteristics. They also have difficulty modeling how target intent changes over different time periods. To address these limitations, this paper develops a deep learning framework that learns motion and semantic information separately and combines historical observations from multiple time ranges. A one-dimensional convolutional network extracts local movement patterns from continuous motion variables, while an embedding layer represents discrete semantic variables as dense feature vectors. These representations are synchronized in time and jointly analyzed by a Transformer. The model selectively uses recent, intermediate, distant, and overall historical information to capture instantaneous maneuvers, changes in behavioral stages, and long-term tactical trends. Experiments on an AFSIM-generated dataset show that the proposed method achieves higher recognition accuracy and better robustness than representative baseline models. Further analysis confirms that both the separate processing of motion and semantic information and the use of multiple historical time ranges contribute to the performance improvements.