Beyond covariance realism: a new metric for uncertainty realism

Joshua T. Horwood; Jeffrey M. Aristoff; Navraj Singh; Aubrey B. Poore; Matthew D. Hejduk

doi:10.1117/12.2054268

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13 June 2014 Beyond covariance realism: a new metric for uncertainty realism

Joshua T. Horwood, Jeffrey M. Aristoff, Navraj Singh, Aubrey B. Poore, Matthew D. Hejduk

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Proceedings Volume 9092, Signal and Data Processing of Small Targets 2014; 90920F (2014) https://doi.org/10.1117/12.2054268
Event: SPIE Defense + Security, 2014, Baltimore, Maryland, United States

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.

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Joshua T. Horwood, Jeffrey M. Aristoff, Navraj Singh, Aubrey B. Poore, and Matthew D. Hejduk "Beyond covariance realism: a new metric for uncertainty realism", Proc. SPIE 9092, Signal and Data Processing of Small Targets 2014, 90920F (13 June 2014); https://doi.org/10.1117/12.2054268

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