Recovering area-to-mass ratio of resident space objects through data mining

The area-to-mass ratio (AMR) of a resident space object (RSO) is an important parameter for improved space situation awareness capability due to its effect on the non-conservative forces including the atmosphere drag force and the solar radiation pressure force. However, information about AMR is often not provided in most space catalogs. The present paper investigates recovering the AMR information from the consistency error, which refers to the difference between the orbit predicted from an earlier estimate and the orbit estimated at the current epoch. A data mining technique, particularly the random forest (RF) method, is used to discover the relationship between the consistency error and the AMR. Using a simulation-based space catalog environment as the testbed, this paper demonstrates that the classification RF model can determine the RSO's category AMR and the regression RF model can generate continuous AMR values, both with good accuracies. Furthermore, the paper reveals that by recording additional information besides the consistency error, the RF model can estimate the AMR with even higher accuracy.