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Optimal Reduction of Multivariate Dirac Mixture Densities by Uwe D. Hanebeck is a Engineering article available to read on EtoBox.

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## Abstract This paper is concerned with the optimal approximation of a given multivariate Dirac mixture, i.e., a density comprising weighted Dirac distributions on a continuous domain, by a Dirac mixture with a reduced number of components. The parameters of the approximating density are calculated by numerically minimizing a smooth distance measure, a generalization of the well-known Cramér–von Mises-Distance to the multivariate case. This generalization is achieved by defining an alternative to the classical cumulative distribution, the Localized Cumulative Distribution (LCD), as a smooth characterization of discrete random quantities (on continuous domains). The resulting approximation method provides the basis for various efficient nonlinear estimation and control methods.

Who reads Optimal Reduction of Multivariate Dirac Mixture Densities?

Author

Uwe D. Hanebeck

Publisher

Walter de Gruyter GmbH

Published

2015

Language

EN

Field

Engineering (Physical Sciences)