Discrepancies between the true Martian atmospheric density and the onboard density model can significantly impair the performance of spacecraft entry navigation filters. This work introduces a new approach to online filtering for Martian entry by using a neural network to estimate atmospheric density and employing a consider analysis to account for the uncertainty in the estimate. The network is trained on an exponential atmospheric density model, and its parameters are dynamically adapted in real time to account for any mismatches between the true and estimated densities. The adaptation of the network is formulated as a maximum likelihood problem, leveraging the measurement innovations of the filter to identify optimal network parameters. The incorporation of a neural network enables the use of stochastic optimizers known for their efficiency in the machine learning domain within the context of the maximum likelihood approach. Performance comparisons against previous approaches are conducted in various realistic Mars entry navigation scenarios, resulting in superior estimation accuracy and precise alignment of the estimated density with a broad selection of realistic Martian atmospheres sampled from perturbed Mars-GRAM data.

翻译：真实火星大气密度与机载密度模型之间的偏差会显著影响航天器进入导航滤波器的性能。本文提出一种新的火星进入在线滤波方法：通过神经网络估计大气密度，并采用考虑分析来评估该估计的不确定性。该网络基于指数大气密度模型进行训练，其参数实时动态调整以应对真实密度与估计密度之间的任何失配。网络自适应被形式化为最大似然问题，利用滤波器的量测新息来识别最优网络参数。神经网络的引入使得能够在最大似然方法框架下使用机器学习领域以高效著称的随机优化器。在多种实际火星进入导航场景中与现有方法进行性能对比，结果表明该方法具有更优的估计精度，并且估计密度能够与从扰动火星全球参考大气模型数据中采样得到的广泛真实火星大气准确匹配。