Beyond covariance realism: a new metric for uncertainty realism
Joshua T. Horwood, Jeffrey M. Aristoff, Navraj Singh, Aubrey B. Poore, Matthew D. Hejduk · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
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.