A Novel Gaussian Mixture Approximation for Nonlinear Estimation
Renato Zanetti, Kirsten Tuggle · 2018
A novel adaptive nonlinear estimator is presented to accurately incorporate nonlinear/non-Gaussian measurement in a Bayesian framework. The underlying algorithm relies on a Gaussian Mixture Model (GMM) to approximate the probability density function (pdf) of the state conditioned on all current and past measurement. Automatic mixture components refining is performed to ensure that the posterior GMM approximation of the pdf accurately represents the true distribution.