Highlights: A modification of the Cube algorithm is proposed to deal with Low-Earth Orbit medium-term collision risk assessment. The method translates the uncertainty in the object’s position in a set of cubes in which the object could potentially be. Performance is assessed in different scenarios for varying the area-to-mass ratio of the objects, as well as the atmospheric density model used. Performance is compared to that of the original Cube algorithm. An estimation of the degree of confidence associated to a potential conjunction is provided.

    Abstract The number of Earth orbiting objects is constantly growing, and some orbital regions are becoming risky environments for space assets of interest, which are increasingly threatened by accidental collisions with other objects, especially in Low-Earth Orbit (LEO). Collision risk assessment is performed by various methods, both covariance and non-covariance based. The Cube algorithm is a non-covariance-based method used to estimate the collision rates between space objects, whose concept consists in dividing the space in cubes of fixed dimension and, at each time instant, checking if two or more objects share the same cube. Up to now its application has been limited to the long-term scenarios of orbital debris evolutionary models, where considering the uncertainties is not necessary and impractical. Within operative contexts, instead, medium-term collision risk analysis may be an important task, in which the propagation-related uncertainties play a prominent role, but the timescale poses challenges for the application of standard covariance-based conjunction analysis techniques. In this framework, this paper presents an approach for the evaluation of the medium-term collision frequency for objects in LEO, called Uncertainty-aware Cube method. It is a modified version of the Cube, able to take the possible errors in the space objects’ position into account for the detection of the conjunctions. As an object’s orbit is propagated, the along-track position error grows more and more, and each object could potentially be in a different position with respect to the one determined by numerical propagation and, thus, in a different cube. Considering the uncertainties, at each time instant the algorithm associates more than one cube to each object and checks if they share at least one cube. If so, a conjunction is detected and a degree of confidence is evaluated. The performance of the method is assessed in different LEO scenarios and compared to the original Cube method.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Uncertainty-aware Cube algorithm for medium-term collision risk assessment


    Beteiligte:
    Isoletta, G. (Autor:in) / Opromolla, R. (Autor:in) / Fasano, G. (Autor:in)

    Erschienen in:

    Advances in Space Research ; 71 , 1 ; 539-555


    Erscheinungsdatum :

    2022-09-06


    Format / Umfang :

    17 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Interaction-aware Predictive Collision Detector for Human-aware Collision Avoidance

    Genevois, Thomas / Spalanzani, Anne / Laugier, Christian | IEEE | 2023


    Collision risk assessment method and system and storage medium

    MA XIAOWEI / ZHANG SHUQI / TIAN HE et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    COLLISION RISK CALCULATION METHOD, STORAGE MEDIUM, AND COLLISION RISK CALCULATION DEVICE

    WATANABE ISAMU | Europäisches Patentamt | 2021

    Freier Zugriff

    A New Representation of Uncertainty for Collision Assessment

    Delande, Emmanuel / Jah, Moriba / Jones, Brandon | TIBKAT | 2019