基于GAN-LSTM的通用机场冲突探测与智能解脱方法OA

To enhance the conflict detection and resolution capability in dynamic environments of Category A general aviation terminal areas,this paper proposes an end-to-end conflict detection and intelligent resolution method based on the deep integration of Generative Adversarial Networks(GAN)and Long Short-Term Memory(LSTM).The core innovations of the method are:①constructing a dual-task discriminator architecture that performs both trajectory authenticity discrimination and conflict probability prediction through shared feature representation;②designing a physics-constrained generator that produces diverse resolution trajectories under flight performance constraints and selects the optimal solution via a multi-criteria screening mechanism;③proposing an adaptive loss weight adjustment strategy to dynamically balance multiple objectives such as trajectory reconstruction accuracy,adversarial training stability,and conflict avoidance.Comprehensive experiments based on the TrajAir dataset show that the proposed method achieves a conflict detection accuracy of 93.4%and a resolution success rate of 88%,significantly outperforming conventional geometric rule-based methods.The generated trajectories exhibit small errors,comply with flight performance constraints,demonstrating the real-time capability,accuracy,and decision-making flexibility of the method.This study provides a feasible technical solution for intelligent air traffic management in general aviation,contributing to safer and more efficient low-altitude airspace operations.