Electrooptical sensors, when used to track space objects, are often used to produce detections of space objects for some orbit determination scheme. Instead, this paper proposes a series of methods to use electrooptical images directly in orbit determination. This work uses the signal-to-noise ratio optimal image filter, called a matched filter, to search for partially known space objects. By defining a metric for measuring matched filter template similarity, a bank of matched filters is efficiently defined by partitioning the prior knowledge set. Once partitioned sets are known, the matched filter bank can be localized to regions of the sky. A method for hypothesis testing the result of a matched filter for a space object is developed. Finally, a framework for orbit determination based on the matched filter result is developed. Simulation shows that the analytic results enable a better framework for implementing matched filters for low-signal-to-noise ratio object detection.


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    Titel :

    Space Object Detection in Images Using Matched Filter Bank and Bayesian Update


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    01.03.2017




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






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