Uncorrelated-Track Classification, Characterization, and Prioritization Using Admissible Regions and Bayesian Inference
Marcus J. Holzinger, Kim Luu, Chris Sabol, Keric A. Hill · Journal of Guidance Control and Dynamics · 2016
This paper introduces and discusses a method to rigorously classify and prioritize uncorrelated tracks using Bayesian inference and admissible regions. A detailed derivation and discussion of the methodology are given, followed by a generalized definition of prioritization parameters. Several example prioritization parameters, including time left to detect, zero-effort miss, and effective albedo–area, are motivated and given. A number of illustrative applications with optical uncorrelated tracks are examined to demonstrate information that can be extracted from each observation. Finally, the information extracted from each uncorrelated track is then compared and prioritization of subsequent sensor-asset measurements discussed.