Abstract
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.
Highlights
Low Earth orbiting (LEO) satellite orbit is affected by many perturbing forces, among which the atmospheric drag has the largest uncertainty [1]
The appropriated time delay and the sample rate of the long short-term memory (LSTM)-NRL model are found by parameter tuning method Using the data on 31 January 2007 as the test set, Test 1 evaluates the performance of the LSTM-NRL with different TMSISE and ts, and the two parameters resulting in the best model performance are chosen
Three density series are used in the Challenging Minisatellite Payload (CHAMP) orbit determination and prediction: the density calibrated by the LSTM-NRL, the density computed from the NRLMISIE-00 model, and the “true” density derived from the CHAMP accelerometer data
Summary
Low Earth orbiting (LEO) satellite orbit is affected by many perturbing forces, among which the atmospheric drag has the largest uncertainty [1]. The astrodynamics community mostly uses empirical models to compute atmospheric mass density in the orbital propagation of LEO satellites. The U.S Air Force Space Battlelab’s high accuracy satellite drag model (HASDM) uses radar tracking data of 75 satellites from the space surveillance network (SSN) to calibrate the Jacchia-70 model, reducing the error of density to 6~8% at altitude ranges from 200 km to 800 km. Gao et al use the Gaussian process method to calibrate the NRLMSISE-00 and JB-2008, and a framework is developed to estimate the atmospheric density based on empirical models, space environmental conditions, and satellite measurements [24]. Chen et al use artificial neural network to calibrate the density model during magnetic storms; the accuracy of the short-term orbit prediction is superior to those using JB-2008 and NRLMSISE-00 [25].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
