Long-range masked autoencoder for pre-extraction of trajectory features in within-visual-range maneuver recognition

In the field of intelligent air combat, real-time and accurate recognition of within-visual-range (WVR) maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems. However, existing methods face two major challenges: traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling, making it difficult to distinguish essential differences between maneuvers, while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies. This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder (LMAE), incorporating three key innovations: (1) Random Fragment High-ratio Masking (RFH-Mask), which enforces the model to learn long-range temporal correlations by masking 80% of trajectory data while retaining continuous fragments; (2) Kalman Filter-Guided Objective Function (KFG-OF), integrating trajectory continuity constraints to align the feature space with kinematic principles; and (3) Two-stage Decoupled Architecture, enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer. Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models, while significantly accelerating convergence speed. The contributions of this work lie in: introducing high-masking-rate autoencoders into low-information-density trajectory analysis, proposing a feature engineering framework with enhanced controllability and efficiency, and providing a novel technical pathway for intelligent air combat decision-making systems.