Optimal measurement filtering and motion prediction taking into account the atmospheric perturbations

The primary errors in orbit determination and prediction for Low Earth Orbit space objects result from the inaccuracy of the upper atmosphere density models and the absence of concrete data about the variations of the ballistic coefficients. The authors investigate a convenient method for orbit determination taking into account 'colored noise' statistical characteristics of the atmospheric disturbances. This technique, named "Optimal Measurement Filtering" (OMF), has some common features with both the Least Squares Technique and the Kalman filter. The a priori correlation function of the atmospheric noise is given in the form of a numerical table that enables one to change it without altering the algorithm. For forecasting, the special corrections are added to the results of the integration of the equations of motion.

Optimal measurement filtering and motion prediction taking into account the atmospheric perturbations Conference Paper