The Split and Merge Unscented Gaussian Mixture Filter

Abstract

In this work we present a novel approach to nonlinear, non-Gaussian tracking problems based on splitting and merging Gaussian filters in order to increase the level of detail of the filtering density in likely regions of the state space and reduce it in unlikely ones. As this is only effective in the presence of nonlinearities, we describe a split control technique that prevents filters from being split if they operate in linear regions of state space. In simulations with polar measurements, the new algorithm reduced the mean square error by nearly 50% compared to the unscented Kalman filter.