Probabilistic Tracklet Characterization and Prioritization Using Admissible Regions.

摘要

This paper introduces and discusses a method to rigorously classify and prioritize UCTs using Bayesian inference and admissible regions. A detailed derivation and discussion of the methodology is given, followed by a generalized de nition of prioritization parameters. Several example prioritization parameters including `time left to detect,' `zero-e ort miss,' and `e ective albedo-area' are motivated and given. A number of illustrative applications with optical UCTs are examined to demonstrate information that can be extracted from each observation. Finally, the information extracted from each UCT is then compared and approaches to observation prioritization discussed.