Orbit covariance prediction based on numerical orbit model

The orbital covariance information of debris is wildly used in projects such as uncorrelated tracks catalog and spacecraft collision warning to calculate collision probability. Orbital covariance information contains initial orbit error, measurement equipment error and perturbation model error. It's very important to make a prediction for the covariance above. In the paper, covariance analysis was conducted on low earth orbital objects, International Space Station(ISS) and Japanese satellite AJISAI. The covariance prediction was made through UT transform method of Unscent Kalman Filter and linear covariance method based on Jacobian transform. The simulation results shows that for a period of 200 minutes time, through UKF method the covariance prediction accuracy of ISS increases. But for satellite AJISAI covariance predicted by UKF and linear methods are almost the same. And then, the covariance prediction result from the two methods was compared. At last, through Monte-Carlo method the accuracy of the covariance prediction was verified.