This study aims to enhance Low Earth Orbit (LEO) satellite orbit prediction accuracy. We propose the Precise Orbit Determination with Optimized Perturbations (PODOP) method, considering Earth’s non-spherical gravity, atmospheric drag, etc., and a Long Short-Term Memory (LSTM)-based approach for orbital element time series. Validation shows that PODOP’s 10-day median error is 8.1 km (19% larger than Simplified General Perturbations (SGP4)’s 10.1 km) and LSTM’s 10-day median error is 5.3 km, outperforming SGP4 (48.5 km) and PODOP and improving constellation management and collision prevention.

为提升TLE(两行根数)在轨道预报中的精度, 解决传统多项式拟合和物理建模方法在难以处理轨道非线性演化趋势时的问题, 提出一种基于长短期记忆网络(LSTM)的高精度TLE轨道参数拟合方法, 利用历史TLE数据对轨道要素进行高精度时间序列拟合与轨道合成. 采用LSTM神经网络对TLE轨道参数进行时间序列建模, 并结合多项式拟合形成混合建模策略. 实验以87个铱星33碎片的TLE数据为基础, 构建拟合模型并生成合成TLE利用SGP4传播器正向传播位置误差. 实验结果表明, 常规时段81个碎片合成TLE传播7天位置误差改善目标占比达65.43%～88.89%, 误差分布更集中稳定; 磁暴期间65个碎片的合成TLE传播3天位置误差改善目标占比为70.77%～80.00%, 误差波动范围低于原始TLE. 基于LSTM的TLE轨道合成方法在捕捉轨道参数非线性演化方面表现优越, 结合角度线性化和平滑滤波策略, 提高了轨道参数拟合的精度与稳定性.