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Support-Vector Conditional Density Estimation for Nonlinear Filtering

Konferenzbeitrag

Links:
Autoren:

Peter Krauthausen
Marco F. Huber
Uwe D. Hanebeck

Quelle:

Proceedings of the 13th International Conference on Information Fusion (Fusion), 2010.

Konferenz:

13th International Conference on Information Fusion (Fusion), Edinburgh, United Kingdom, 26.-29. Juli 2010

A non-parametric conditional density estimation algorithm for nonlinear stochastic dynamic systems is proposed. The contributions are a novel support vector regression for estimating conditional densities, modeled by Gaussian mixture densities, and an algorithm based on cross-validation for automatically determining hyper-parameters for the regression. The conditional densities are employed with a modified axis-aligned Gaussian mixture filter. The experimental validation shows the high quality of the conditional densities and good accuracy of the proposed filter.