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Proceedings Paper

Beyond covariance realism: a new metric for uncertainty realism
Author(s): Joshua T. Horwood; Jeffrey M. Aristoff; Navraj Singh; Aubrey B. Poore; Matthew D. Hejduk
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Paper Abstract

In the space surveillance tracking domain, it is often necessary to assess not only the covariance consistency or covariance realism of an object's state estimate, but also the realism (proper characterization) of its full estimated probability density function. In other words, there is a need for “uncertainty realism." We propose a new metric (applicable to any tracking domain) that generalizes the covariance realism metric based on the Mahalanobis distance to one that tests uncertainty realism. We then review various goodness-of-fit and distribution matching tests that exploit the uncertainty realism metric and describe how these tests can be applied to assess uncertainty realism in off-line simulations with multiple Monte-Carlo trials or on-line with real data when truth is available.

Paper Details

Date Published: 13 June 2014
PDF: 14 pages
Proc. SPIE 9092, Signal and Data Processing of Small Targets 2014, 90920F (13 June 2014); doi: 10.1117/12.2054268
Show Author Affiliations
Joshua T. Horwood, Numerica Corp. (United States)
Jeffrey M. Aristoff, Numerica Corp. (United States)
Navraj Singh, Numerica Corp. (United States)
Aubrey B. Poore, Numerica Corp. (United States)
Matthew D. Hejduk, Astrorum Consulting (United States)


Published in SPIE Proceedings Vol. 9092:
Signal and Data Processing of Small Targets 2014
Oliver E. Drummond, Editor(s)

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