Support-vector conditional density estimation for nonlinear filtering

Abstract

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.