IMPLICATIONS OF HIERARCHIES FOR RSO RECOGNITION, IDENTIFICATION, AND CHARACTERIZATION

摘要

In our previous work, we demonstrated that hierarchical (taxonomical) trees can be used to depict hypotheses in a Bayesian object recognition and identification process using Figaro, an open source probabilistic programming language. We assume in this work that we have appropriately defined a satellite taxonomy that allows us to place a given space object (RSO) into a particular class of object without any ambiguity. Such a taxonomy allows one to assess the probability of assignment to a particular class by determining how well the object satisfies the unique criteria of belonging to that class. Furthermore, tree-based taxonomies delineate unique signatures by defining the minimum amount of information required to positively identify a RSO. Because of these properties of taxonomic trees, we can now explore the implications of RSO taxonomic trees for model distance metrics and sensor tasking. In particular, we seek to exploit the fact that taxonomic trees provide a model "neighborhood" that can be used to initiate a Monte Carlo or Multiple Hypothesis algorithm. We contend this feature of taxonomies will provide a quantifiable metric for model distances and the explicit number of models that should be considered, both of which currently do not exist. Additionally, the discriminating characteristics of taxonomic classes can be used to determine the kind of data and the associated sensor that needs to be tasked to acquire that data. We also discuss the concept of multiple interacting hierarchies that provide deeper insight into how object interact with one another.