Dirac Mixture Approximation of Multivariate Gaussian Densities
Uwe D. Hanebeck
Marco F. Huber
Proceedings of the Joint 48th IEEE Conference on Decision & Control and 28th Chinese Control Conference, 2009.
Joint 48th IEEE Conference on Decision & Control and 28th Chinese Control Conference, Shanghai, China, 16.-18. Dezember 2009
For the optimal approximation of multivariate Gaussian densities by means of Dirac mixtures, i.e., by means of a sum of weighted Dirac distributions on a continuous domain, a novel systematic method is introduced. The parameters of this approximate density are calculated by minimizing a global distance measure, a generalization of the well-known Cramér-von Mises distance to the multivariate case. This generalization is obtained by defining an alternative to the classical cumulative distribution, the Localized Cumulative Distribution (LCD). In contrast to the cumulative distribution, the LCD is unique and symmetric even in the multivariate case. The resulting deterministic approximation of Gaussian densities by means of discrete samples provides the basis for new types of Gaussian filters for estimating the state of nonlinear dynamic systems from noisy measurements.