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Adaptive Gaussian Sum Filters for Space Surveillance Tracking

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Abstract

While orbital propagators have been investigated extensively over the last fifty years, the consistent propagation of state covariances and more general (non-Gaussian) probability densities has received relatively little attention. The representation of state uncertainty by a Gaussian mixture is well suited for problems in space situational awareness. Advantages of this approach, which are demonstrated in this paper, include the potential for long-term propagation in data-starved environments, the capturing of higher-order statistics and more accurate representation of nonlinear dynamical models, the ability to make the filter adaptive using real-time metrics, and parallelizability. Case studies are presented establishing uncertainty consistency and the effectiveness of the proposed adaptive Gaussian sum filter.

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Authors and Affiliations

  1. Numerica Corporation, 4850 Hahns Peak Drive, Suite 200, Loveland, Colorado, 80538, U.S.A.

    Joshua T. Horwood, Nathan D. Aragon & Aubrey B. Poore

Authors
  1. Joshua T. Horwood
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  2. Nathan D. Aragon
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  3. Aubrey B. Poore
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Correspondence to Joshua T. Horwood.

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Horwood, J.T., Aragon, N.D. & Poore, A.B. Adaptive Gaussian Sum Filters for Space Surveillance Tracking. J of Astronaut Sci 59, 308–326 (2012). https://doi.org/10.1007/s40295-013-0020-7

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  • DOI: https://doi.org/10.1007/s40295-013-0020-7

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