Support-Vector Conditional Density Estimation for Nonlinear Filtering
Marco F. Huber
Uwe D. Hanebeck
Proceedings of the 13th International Conference on Information Fusion (Fusion), 2010.
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.