An Empirical Atmospheric Density Calibration Model Based on Long Short-Term Memory Neural Network

• Main Authors: Yan Zhang, Jinjiang Yu, Junyu Chen, Jizhang Sang
• Format: Article
• Language: English
• Published: MDPI AG 2021-07-01
• Series: Atmosphere
• Subjects: atmospheric density; calibration model; LSTM; empirical density model
• Online Access: https://www.mdpi.com/2073-4433/12/7/925

The accuracy of the atmospheric mass density is one of the most important factors affecting the orbital precision of spacecraft at low Earth orbits (LEO). Although there are a number of empirical density models available to use in the orbit determination and prediction of LEO spacecraft, all of them suffer from errors of various degrees. A practical way to reduce the error of a particular model is to calibrate the model using precise density data or tracking data. In this paper, a long short-term memory (LSTM) neural network is proposed to calibrate the NRLMSISE-00 density model, in which the densities derived from spaceborne accelerometer data are the main input. The resulted LSTM-NRL model, calibrated using the accelerometer data from Challenging Minisatellite Payload (CHAMP) satellite, is extensively experimented to evaluate the calibration performance. With data in one month to train the LSTM-NRL model, the model is shown to effectively reduce the root mean square error of the model densities outside the training window by more than 40% in various time spans and space weather environment. The LSTM-NRL model is also shown to have remarkable transferring performance when it is applied along the GRACE satellite orbits.