在非弹性碰撞后跟踪一个未被追踪的空间碎片,使用基于物理学的神经网络来追踪
Harsha M1, Gurpreet Singh2, Vinod Kumar3
1Indraprastha Institute of Information Technology Delhi, New Delhi, 110020, India. harsham@iiitd.ac.in.
Scientific reports
|February 9, 2024
概括
物理信息神经网络 (PINNs) 改善了碰撞后未追踪的空间碎片的轨迹估计. 这种方法提高了碎片位置,速度和质量的预测准确度,这对于在低地轨道上避免碰撞至关重要.
科学领域:
- 空间碎片的动态空间碎片的动态
- 天体动力学是指天体动力学.
- 太空监视中的人工智能
背景情况:
- 在低地轨道 (LEO) 上越来越多的卫星部署增加了未被追踪的空间碎片的碰撞风险.
- 小尺寸的空间碎片 (<10厘米) 对当前最先进的追踪方法构成重大挑战.
- 准确地预测太空碎片的轨迹对于防止未来的轨道碰撞至关重要.
研究的目的:
- 开发和评估物理信息神经网络 (PINN) 方法,用于估计撞击后未追踪空间碎片的轨迹.
- 为了比较基于PINN的方法与经典优化和深度神经网络方法进行碎片状态估计的性能.
主要方法:
- 模拟了8565个活跃卫星 (Starlink,LEMUR) 与空间碎片之间的不弹性碰撞事件.
- 利用了活跃卫星状态的TLE数据,并提出了用于碎片速度初始化和归还取样系数的新方法.
- 应用经典优化 (拉格朗奇乘数),深度神经网络 (DNN) 和物理信息神经网络 (PINN) 来估计碰撞后碎片状态 (位置,速度,质量,归还系数).
主要成果:
- 经典的优化方法由于系统的不确定性而被证明是不令人满意的.
- 基于PINN的方法在估计未追踪空间碎片的位置,速度,质量和归还系数方面表现出卓越的性能.
- 使用根平均平方误差 (RMSE) 和四分位数范围量化评估了性能,突出了PINNs的改进准确性.
结论:
- 物理信息神经网络提供了一个强大而准确的解决方案,用于追踪和估计碰撞后小,未被追踪的空间碎片的状态.
- PINN方法显著提高了预测碎片轨迹的能力,有助于改善LEO的空间局势意识和避免碰撞的策略.
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