Covariance and Uncertainty Realism in Space Surveillance and Tracking
Abstract
The characterization of uncertainty in the estimate of the state of a resident space object is fundamental to many space surveillance tasks including data association, uncorrelated track (UCT) resolution, catalog maintenance, sensor tasking and scheduling as well as space situational awareness (SSA) missions such as conjunction assessments and maneuver detection. The need for and importance of uncertainty quantification is established for each of these problem domains. The following eight generic classes of uncertainty are then identified: structural uncertainty or model bias in the model dynamics, uncertain parameters, sensor level errors, inverse uncertainty quantification, propagation of uncertainty, algorithmic or numerical uncertainty, cross-tag or misassociation uncertainty, hardware and software faults/errors. A state-of-the-art assessment is established for most of these areas through a survey of the existing literature. Research and development recommendations are made for both maturing areas of research such as the propagation of uncertainty and less mature areas such sensor level processing. A final chapter summarizes the recommendations and the next steps that promise to improve the overall uncertainty characterization and quantification of resident space objects.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 27, 2016
- Accession Number
- AD1020892
Entities
People
- Audrey B. Poore
- Brandon A. Jones
- Christopher M. Cox
- Daniel J. Scheeres
- David A. Vallado
- Jeffrey M. Aristoff
- Joseph H. Frisbee
- Joshua T. Horwood
- Matt D. Hejduk
- Pierluigi Di Lizia
- Richard S. Erwin
- Roberto Armellin
- Ryan M. Weisman
- William T. Cerven
- Yang Cheng