Sensor management for collision alert in orbital object tracking

Given the increasingly dense environment in both low-earth orbit (LEO) and geostationary orbit (GEO), a sudden change in the trajectory of any existing resident space object (RSO) may cause potential collision damage to space assets. With a constellation of electro-optical/infrared (EO/IR) sensor platforms and ground radar surveillance systems, it is important to design optimal estimation algorithms for updating nonlinear object states and allocating sensing resources to effectively avoid collisions among many RSOs. Previous work on RSO collision avoidance often assumes that the maneuver onset time or maneuver motion of the space object is random and the sensor management approach is designed to achieve efficient average coverage of the RSOs. Few attempts have included the inference of an object's intent in the response to an RSO's orbital change. We propose a game theoretic model for sensor selection and assume the worst case intentional collision of an object's orbital change. The intentional collision results from maximal exposure of an RSO's path. The resulting sensor management scheme achieves robust and realistic collision assessment, alerts the impending collisions, and identifies early RSO orbital change with lethal maneuvers. We also consider information sharing among distributed sensors for collision alert and an object's intent identification when an orbital change has been declared. We compare our scheme with the conventional (non-game based) sensor management (SM) scheme using a LEO-to-LEO space surveillance scenario where both the observers and the unannounced and unplanned objects have complete information on the constellation of vulnerable assets. We demonstrate that, with adequate information sharing, the distributed SM method can achieve the performance close to that of centralized SM in identifying unannounced objects and making early warnings to the RSO for potential collision to ensure a proper selection of collision avoidance action.

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