Advanced ensemble modeling method for space object state prediction accounting for uncertainty in atmospheric density

For objects in the low Earth orbit region, uncertainty atmospheric density estimation is an important source of prediction error, which critical for space traffic management activities such as satellite conjunction analysis. This paper investigates evolution error distribution presence uncertainties, are modeled using probabilistic machine learning techniques. The recently proposed “HASDM-ML,” “CHAMP-ML,” and “MSIS-UQ” models (Licata Mehta, 2022b; Licata et al., 2022b) used this work. investigation convoluted because spatial temporal correlation values. We develop several Monte Carlo methods, each capturing a different spatiotemporal correlation, to study effects on propagation. However, analysis computationally expensive, so faster method based Kalman filtering technique propagation also explored. It difficult translate orbital states under standard extended filter or unscented framework. work uses so-called “consider covariance sigma point (CCSP)” that can account uncertainties during As test-bed validation purposes, comparison between CCSP methods carried out. Finally, HASDM-ML, CHAMP-ML, MSIS-UQ models, we propose ensemble approach quantification four weather conditions.