Towards an Artificial Space Object Taxonomy
Matthew P. Wilkins, Avi Pfeffer, Paul W. Schumacher, Moriba Kemessia Jah · Advanced Maui Optical and Space Surveillance Technologies Conference · 2013
Abstract : Object recognition is the first step in positively identifying a resident space object (RSO), i.e. assigning an RSO to a category such as GPS satellite or space debris. Object identification is the process of deciding that two RSOs are in fact one and the same. Provided we have appropriately defined a satellite taxonomy that allows us to place a given RSO into a particular class of object without any ambiguity, one can assess the probability of assignment to a particular class by determining how well the object satisfies the unique criteria of belonging to that class. Ultimately, tree-based taxonomies delineate unique signatures by defining the minimum amount of information required to positively identify a RSO. Therefore, taxonomic trees can be used to depict hypotheses in a Bayesian object recognition and identification process. This work describes a new RSO taxonomy along with specific reasoning behind the choice of groupings. We will demonstrate how to implement this taxonomy in Figaro, an open source probabilistic programming language.