Precision Mars Entry Navigation with Atmospheric Density Adaptation via
  Neural Networks
Felipe Giraldo-Grueso Andrey A. Popov Renato Zanetti
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.