Comparison of three approximate kinematic models for space object tracking

In this paper we compare the performance of three kinematic state models, i.e., the White Noise Acceleration (WNA), the Wiener process acceleration (WPA), and the Keplerian State (KPS) model, for the tracking of earth orbiting space objects (SOs). The three models considered are all simplified approximate models for the motion of Earth orbiting SOs and are not suitable for the prediction of target tracks for long time periods. However, for track updates with new measurements coming at a high rate, such simplified motion models can be effectively used with small or no loss in estimation accuracy. For the KPS model, we use a novel mixed-coordinate SO tacking (McSOT) filter, where the target state space is defined in the Cartesian, i.e., Earth-Central Inertial (ECI), coordinates for track representation and updates, while the track propagation is done in the Keplerian Coordinates. It is shown that when the measurement accuracy is high, the McSOT filter with the KPS model, which has the highest complexity among the three, is able to achieve significantly better estimation accuracy than the filters with the WNA and WPA models. The WPA model is able to achieve better tracking accuracy than the WNA model at the cost of moderate increase of algorithm complexity. On the other hand, when the measurement accuracy is low, the filters with the WNA and WPA models which operate solely in the Cartesian coordinates, i.e., the Earth-Central Inertial (ECI) coordinates, is more robust than the McSOT filter with the KPS model.