基于孪生长短期记忆网络的角轨迹快速关联方法OA

With the rapid growth in the number of space objects,the utilization of space-based optical systems for space object positioning holds significant importance for space security.As a critical component in multi-satellite collaborative positioning,angular trajectory association faces challenges with traditional geometry or kinematics-based methods,including combinatorial explosion,noise sensitivity,and poor real-time performance.This paper proposes a deep learning-based fast angular trajectory association method.By constructing multi-satellite multi-target simulation scenarios,simulated angular trajectory data are generated,and a Siamese long short-term memory network is designed for angular trajectory association.Experimental results demonstrate that,compared with traditional methods,the proposed method improves accuracy by about 3%and reduces inference time by more than 9 times.Therefore,this method can significantly increase the association speed of space multi-target trajectories while maintaining high accuracy,providing a reference for research on space objects'trajectory association.