Given partial observation of a space object, the admissible region is defined as the set of all physically acceptable orbits that are consistent with the observation. Given additional constraints on the object’s orbital semimajor axis and eccentricity, the admissible region can be further constrained, resulting in the constrained admissible region. All points within that region are traditionally treated as equally likely and are used to initialize a multihypothesis filter. In this paper, known statistics of the measurement process as well as space object population semimajor axis and eccentricity values are used to obtain a more representative distribution over the constrained region. This nonuniform distribution results in faster convergence of the multihypothesis filter because unlikely hypotheses are promptly eliminated. It is also shown how known statistics in inclination and right ascension of the ascending node can be included to incorporate additional constraints on other orbital parameters. The probabilistic admissible region is a thorough approach to orbit initiation in Bayesian tracking within the context of multihypothesis filtering. Noting that the concepts presented in this paper can be applied to many measurement scenarios, the idea is illustrated using a short-arc, angles-only observation scenario.


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    Title :

    Probabilistic Admissible Region for Multihypothesis Filter Initialization



    Published in:

    Publication date :

    2018-03-01




    Type of media :

    Conference paper , Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English