Object Correlation, Maneuver Detection, and Maneuver Characterization using Control Effort Metrics with Uncertain Boundary Conditions and Measurements

Abstract

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 quadratic trajectory control cost, an analog to the trajectory L2-norm in control, as a distance metric with which to correlate object observations, detect maneuvers, and characterize maneuvers using real-time sensor measurement residuals and prior state uncertainty. An object track correlation approach is investigated that frames the Two-Point Boundary Value Problem (TPBVP) with uncertain boundary conditions in terms of the control distance metric framework. Also, information typically available to operators such as propagated state uncertainty, measurement uncertainty, and real-time measurement residuals are also incorporated into the control distance metric framework. Careful inclusion of these uncertainties enables conservative maneuver detection and characterization. Simulated examples of the approaches are given and implications are discussed. Potential avenues for future research and contributions are summarized.