Density Estimation for Entry Guidance Problems using Deep Learning
Jens A. Rataczak Davide Amato Jay W. McMahon
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.