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Covariance Manipulation for Conjunction AssessmentThe manipulation of space object covariances to try to provide additional or improved information to conjunction risk assessment is not an uncommon practice. Types of manipulation include fabricating a covariance when it is missing or unreliable to force the probability of collision (Pc) to a maximum value ('PcMax'), scaling a covariance to try to improve its realism or see the effect of covariance volatility on the calculated Pc, and constructing the equivalent of an epoch covariance at a convenient future point in the event ('covariance forecasting'). In bringing these methods to bear for Conjunction Assessment (CA) operations, however, some do not remain fully consistent with best practices for conducting risk management, some seem to be of relatively low utility, and some require additional information before they can contribute fully to risk analysis. This study describes some basic principles of modern risk management (following the Kaplan construct) and then examines the PcMax and covariance forecasting paradigms for alignment with these principles; it then further examines the expected utility of these methods in the modern CA framework. Both paradigms are found to be not without utility, but only in situations that are somewhat carefully circumscribed.
Document ID
20160010497
Acquisition Source
Goddard Space Flight Center
Document Type
Conference Paper
Authors
Hejduk, M. D. (Astrorum Consulting, LLC Waco, TX, United States)
Date Acquired
August 22, 2016
Publication Date
September 13, 2016
Subject Category
Astrodynamics
Statistics And Probability
Report/Patent Number
GSFC-E-DAA-TN34094
Meeting Information
Meeting: AIAA Space and Astronautics Forum and Exposition (SPACE 2016)
Location: Long Beach, CA
Country: United States
Start Date: September 13, 2016
End Date: September 16, 2016
Sponsors: American Inst. of Aeronautics and Astronautics
Funding Number(s)
CONTRACT_GRANT: NNG14VC09C
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
Conjunction
Covariance
Assessment

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