Support-Vector Conditional Density Estimation for Nonlinear Filtering

A non-parametric conditional density estimation algorithm for nonlinear stochastic dynamic systems is proposed. The contributions are a novel sup- port vector regression for estimating conditional den- sities, 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 modi?ed axis- aligned Gaussian mixture filter. The experimental va- lidation shows the high quality of the conditional densi- ties and good accuracy of the proposed filter.