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• W2127803704 abstract "In this paper, we propose a progressive Bayesian procedure, where the measurement information is continuously included into the given prior estimate (although we perform observations at discrete time steps). The key idea is to derive a system of ordinary first-order differential equations (ODE) by employing a new coupled density representation comprising a Gaussian density and its Dirac Mixture approximation. The ODE is used for continuously tracking the true non-Gaussian posterior by its best matching Gaussian approximation. The performance of the new filter is evaluated in comparison with state-of-the-art filters by means of a canonical benchmark example, the discrete-time cubic sensor problem." @default.
• W2127803704 created "2016-06-24" @default.
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• W2127803704 date "2012-03-31" @default.
• W2127803704 modified "2023-09-27" @default.
• W2127803704 title "Progressive Gaussian Filtering" @default.
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