The Split and Merge Unscented
Friedrich Faubel, John W. McDonough, Dietrich Klakow · 2009
Inthisworkwepresentanovelapproachtononlinear, non-Gaussian tracking problems based on splitting and merging Gaussian filters in order to increase the level of detail of the fil- tering density in likely regions of the state space and reduce it in unlikely ones. As this is only effective in the presence of nonlin- earities, 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.