Object Correlation, Maneuver Detection, and Characterization Using Control-Distance Metrics

Object correlation, maneuver detection, and maneuver characterization are persistent problems in space surveillance and space object catalog maintenance. This paper demonstrates the utility of using control effort as a rigorously defined metric with which to correlate object observations, detect maneuvers, and characterize maneuvers given dynamical systems with boundary-condition uncertainty. Uncorrelated tracks and new object measurements are incorporated into the control-distance metric framework and corresponding control-distance distributions are computed. Approaches are given with which to rank control-distance distributions and hypothesis testing is used to detect possible maneuvers in the presence of system uncertainty. Simulated examples of the approaches are given and implications are discussed. Potential avenues for future research and contributions are summarized.