The reconstruction of atmospheric properties encountered during Mars entry trajectories is a crucial element of postflight mission analysis. This paper proposes a deep learning architecture using a long short-term memory (LSTM) network for the reconstruction of Martian density and wind profiles from inertial measurements and guidance commands. The LSTM is trained on a large set of Mars entry trajectories controlled through the fully numerical predictor-corrector entry guidance (FNPEG) algorithm, with density and wind from the Mars Global Reference Atmospheric Model (GRAM) 2010. The training of the network is examined, ensuring that the LSTM generalizes well to samples not present in the training set, and the performance of the network is assessed on a separate training set. The errors of the reconstructed density and wind profiles are, respectively, within 0.54 and 1.9%. Larger wind errors take place at high altitudes due to the decreased sensitivity of the trajectory in regions of low dynamic pressure. The LSTM architecture reliably reproduces the atmospheric density and wind encountered during descent.

在火星进入轨迹期间遇到的大气特性的重建是飞行后任务分析的关键要素。本文提出了一种使用长短期记忆 (LSTM) 网络的深度学习架构，用于根据惯性测量和制导命令重建火星密度和风廓线。LSTM 在大量火星进入轨迹上进行训练，这些轨迹通过完全数值预测器-校正器进入引导 (FNPEG) 算法控制，密度和风来自火星全球参考大气模型 (GRAM) 2010。检查网络的训练，确保 LSTM 能够很好地泛化到训练集中不存在的样本，并且在单独的训练集上评估网络的性能。重建的密度和风廓线的误差是，分别在 0.54% 和 1.9% 以内。由于在低动态压力区域中轨迹的敏感性降低，因此在高海拔处会出现较大的风误差。LSTM 架构可靠地再现了下降过程中遇到的大气密度和风。