CORRELATION OF OPTICAL OBSERVATIONS TO CATALOGUED OBJECTS USING MULTIPLE HYPOTHESIS FILTERS

One of the key challenges for catalogue maintenance of space debris is to correctly correlate new measurements to their originating objects. This paper describes a workflow to associate optical observations to object for which a prior information is available in a robust manner. For efficiency, observations are compressed into atributable vectors and compared against known objects in the measurement space. A pre-filter is used to early eliminate non feasible candidates based on physical constraints and on information theoretic metrics. Suitable candidates are used in a Multiple Hypothesis Bayesian framework to perform the association based on the highest likelihood. The use of a Multiple Hypothesis Filter improves the robustness of the process for the case of ambiguous association of closely spaced objects. Different pre-filter algorithms are described. Results of applying this workflow are shown for GEO and MEO targets.