Low-Earth-orbit (LEO) spacecraft are significantly influenced by atmospheric drag. Accurately estimating thermospheric density is pivotal for the precise calculation of drag acceleration. However, thermospheric density along a specific orbit, computed using existing thermospheric models, has certain inaccuracies. In this work, a first-order Gauss–Markov process is used to model the deviation of atmospheric drag acceleration. With the Markov parameter of the initial state iteratively computed through sequential estimation and the smoothing method, the thermospheric density is derived from high-precision GPS measurements. In simulation scenarios, the root-mean-square error and relative error of the estimated thermospheric density reduce by about 45 and 50% relative to the prior density, respectively. Using the estimated density for orbit propagation, satellite trajectories’ one-day position and velocity error are, respectively, within 100 m and 0.1 m/s, and an average improvement in orbit precision is over 80%. The proposed method has been applied to the real Tsinghua Science Satellite (Q-SAT) GPS measurements for effectiveness verification. It shows strong adaptability under extreme space weather and during the occurrence of geomagnetic storms. Due to the estimated Markov parameter of the initial state obeying the Langevin dynamics properties, the proposed method also offers short-term thermospheric density forecasting potential.

低地球轨道（LEO）航天器受到大气阻力的显着影响。准确估计热层密度对于精确计算阻力加速度至关重要。然而，使用现有热层模型计算的沿特定轨道的热层密度存在一定的不准确性。在这项工作中，使用一阶高斯-马尔可夫过程来模拟大气阻力加速度的偏差。通过序贯估计和平滑方法迭代计算初始状态的马尔可夫参数，从高精度 GPS 测量中推导出热层密度。在模拟场景中，估计热层密度的均方根误差和相对误差相对于先前密度分别减少了约 45% 和 50%。利用估计密度进行轨道传播，卫星轨迹一日位置误差和速度误差分别在100 m和0.1 m/s以内，轨道精度平均提高80%以上。该方法已应用于真实的清华科学卫星（Q-SAT）GPS测量中，验证了其有效性。它在极端太空天气和地磁暴发生期间表现出很强的适应性。由于初始状态的估计马尔可夫参数服从朗之万动力学特性，该方法还提供了短期热层密度预测的潜力。