Calibration of atmospheric density model using two-line element data

Abstract

For satellites in orbits, most perturbations can be well modeled, however the inaccuracy of the atmospheric density model remains the biggest error source in orbit determination and prediction. The commonly used empirical atmospheric density models, such as Jacchia, NRLMSISE, DTM, and Russian GOST, still have a relative error of about 10%–30%. Because of the uncertainty in the atmospheric density distribution, high accuracy estimation of the atmospheric density cannot be achieved using a deterministic model. A better way to improve the accuracy is to calibrate the model with updated measurements. Twoline element (TLE) sets provide accessible orbital data, which can be used in the model calibration. In this paper, an algorithm for calibrating the atmospheric density model is developed. First, the density distribution of the atmosphere is represented by a power series expansion whose coefficients are denoted by the spherical harmonic expansions. Then, the useful historical TLE data are selected. The ballistic coefficients of the objects are estimated using the BSTAR data in TLEs, and the parameterized model is calibrated by solving a nonlinear least squares problem. Simulation results show that the prediction error is reduced using the proposed calibration algorithm.