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@article{Amato:2021:10.2514/1.I010922,
author = {Amato, D and McMahon, JW},
doi = {10.2514/1.I010922},
journal = {Journal of Aerospace Information Systems},
pages = {1--1},
title = {Deep learning method for Martian atmosphere reconstruction},
url = {http://dx.doi.org/10.2514/1.I010922},
volume = {18},
year = {2021}
}

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TY  - JOUR
AB  - 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.
AU  - Amato,D
AU  - McMahon,JW
DO  - 10.2514/1.I010922
EP  - 1
PY  - 2021///
SN  - 2327-3097
SP  - 1
TI  - Deep learning method for Martian atmosphere reconstruction
T2  - Journal of Aerospace Information Systems
UR  - http://dx.doi.org/10.2514/1.I010922
UR  - https://arc.aiaa.org/doi/10.2514/1.I010922
UR  - http://hdl.handle.net/10044/1/91422
VL  - 18
ER  -

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