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.

使用神经网络进行在线滤波，以估计火星大气密度并根据估计的不确定性进行考虑分析，提高实时性能和精确对齐估计密度与多样的火星大气情景的最大似然方法。

通过神经网络实现大气密度自适应的火星进入精确导航

Precision Mars Entry Navigation with Atmospheric Density Adaptation via  Neural Networks

This work presents a deep-learning approach to estimate atmospheric density
profiles for use in planetary entry guidance problems. A long short-term memory
(LSTM) neural network is trained to learn the mapping between measurements
available onboard an entry vehicle and the density profile through which it is
flying. Measurements include the spherical state representation, Cartesian
sensed acceleration components, and a surface-pressure measurement. Training
data for the network is initially generated by performing a Monte Carlo
analysis of an entry mission at Mars using the fully numerical
predictor-corrector guidance (FNPEG) algorithm that utilizes an exponential
density model, while the truth density profiles are sampled from MarsGRAM. A
curriculum learning procedure is developed to refine the LSTM network's
predictions for integration within the FNPEG algorithm. The trained LSTM is
capable of both predicting the density profile through which the vehicle will
fly and reconstructing the density profile through which it has already flown.
The performance of the FNPEG algorithm is assessed for three different density
estimation techniques: an exponential model, an exponential model augmented
with a first-order fading-memory filter, and the LSTM network. Results
demonstrate that using the LSTM model results in superior terminal accuracy
compared to the other two techniques when considering both noisy and noiseless
measurements.

本研究提出了一种深度学习方法，用于估计行星进入引导问题中的大气密度剖面。通过训练 LSTM 神经网络，学习了航天器上可用的测量数据与密度剖面之间的映射关系。该方法优于其他两种技术，在考虑到有噪声和无噪声测量时，从终端精度上对 FNPEG 算法进行了评估。