
handle: 11311/1261783
The increasing number of objects in Earth orbit has encouraged the development of space surveillance and tracking (SST) applications. A critical aspect of SST is the identification and characterization of close encounters between pairs of space objects. The present work introduces a tool for the analysis of conjunctions, consisting of several modules. The first module, which has been shown to greatly speed up the process, employs a series of geometric and temporal filters to shorten the list of potential colliding pairs. The remaining objects are then propagated to compute important parameters such as time of closest approach (TCA), miss distance (MD), and probability of collision (PoC), the latter using three different methods. When a conjunction assessment returns an MD or a PoC that exceeds predefined alert thresholds, the algorithm enables the planning of an impulsive collision avoidance maneuver (CAM) at specific maneuver epochs. CAM candidates are determined using an analytical Keplerian approach, with the goal of achieving the desired PoC or MD. The user can then verify the performance of a specific candidate through perturbed propagation, and the MD and PoC are recalculated after the maneuver to ensure that they meet the desired thresholds. In conclusion, this paper evaluates the performance of the tool using synthetic and real data, providing valuable insights into its effectiveness.
space traffic management, space debris, TL1-4050, conjunction analysis, space surveillance and tracking, MOID, resident space objects, space situational awareness, probability of collision, EUSST, collision avoidance, Motor vehicles. Aeronautics. Astronautics
space traffic management, space debris, TL1-4050, conjunction analysis, space surveillance and tracking, MOID, resident space objects, space situational awareness, probability of collision, EUSST, collision avoidance, Motor vehicles. Aeronautics. Astronautics
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
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