Space Situational Awareness

Realistic Uncertainty Analysis for Sparsely Tracked Space Objects

Many space objects are difficult to track. Often times the objects are small and can only be observed by a few sensors, which in tern leads to a poor orbit solution. However, these 'sparsely tracked' objects are still in the cataloged and do pose a collision threat to other space assets, so realistic state and state uncertainty information must be produced.

SpaceNav's approach to state estimation via sequential processing produces realistic uncertainty predictions.   The estimation approach spans various filtering methodologies; include utilization of the Unscented Kalman Filter. Realistic state uncertainty enables accurate track correlation and collision probability calculations. The state estimates are compared to reference orbits for accuracy comparisons when available. We map our results into ‘collision probability space’ in order to quantify how the collision metrics change as a function of the orbit determination method used.