The Sliced Gaussian Mixture Filter with adaptive state decomposition depending on linearization error

In this paper, a novel nonlinear/nonlinear model decomposition for the Sliced Gaussian Mixture Filter is presented. Based on the level of nonlin-earity of the model, the overall estimation problem is decomposed into a "severely" nonlinear and a "slightly" nonlinear part, which are processed by different estimation techniques. To further improve the efficiency of the estimator, an adaptive state decomposition algorithm is introduced that allows decomposition according to the linearization error for nonlinear system and measurement models. Simulations show that this approach has orders of magnitude less complexity compared to other state of the art estimators, while maintaining comparable estimation errors.