关键词: 全域火力场, 态势认知, 超视距空战, 威胁预测, 动态逃逸, 深度学习

Abstract: To address the threat prediction issue in beyond-visual-range air combat situational awareness, a global fire field-based threat prediction and dynamic evasion methodology is proposed. Firstly, a vector field theory-driven all-domain fire field modeling framework is established, formally defining the concepts and mathematical models of single-aircraft all-domain fire field, joint all-domain fire field, and their corresponding evasion fields. Subsequently, a real-time fire field computation method based on Multi-modal Residual Fusion Network (MRFNet) is developed, which resolves the computational bottlenecks and discrete field distortion issues inherent in traditional Monte Carlo approaches through an "offline training-online inference" deep learning par-adigm. Concurrently, a short-term trajectory prediction algorithm employing a Vector Autoregressive Model is introduced to enable real-time forecasting of multivariate flight states for both adversarial and friendly aircraft. The proposed methodology enables real-time computation of global and local threat assessments in complex distributed combat scenarios, providing targeted threat warnings and evasion recommendations. Experimental results demonstrate that the MRFNet-based approach reduces sin-gle fire field computation time from minute-level to millisecond-level while maintaining fitting errors below 5.00e-4, exhibiting excellent data smoothing and extrapolation generalization capabilities. The VAR-based trajectory prediction achieves longi-tude/latitude errors below 8.73e-4, outperforming various state-of-the-art deep learning-based time series prediction methods, with relative error losses remaining under 22% under strong positional deviation interference in threat detection. Comprehensive simulation analyses confirm that the proposed methodology aligns with pilots' cognitive logic in real combat scenarios, demon-strating high fault tolerance, strong robustness, and low latency characteristics. This work shows significant practical value and operational relevance for adapting to complex distributed combat environments.

Key words: all-domain fire field, situational awareness, beyond-visual-range air combat, threat prediction, dynamic evasion, deep learning