Matthew P. Wilkins, (Applied Defense Solutions), Columbia, MD, Avi Pfeffer, Charles River Analytics, Cambridge, MA, Paul W. Schumacher (Air Force Research Laboratory), Kihei, HI, Moriba K. Jah, (Air Force Research Laboratory), Albuquerque, NM
Keywords: space surveillance, space situational awareness, space object identification, space object characterization, Bayesian networks, space object taxonomy
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. An alternative taxonomy was recently presented at the Sixth Conference on Space Debris in Darmstadt, Germany. [1]
Date of Conference: September 10-13, 2013
Track: Orbital Debris