The goal of this work is improving existing and suggesting novel filtering algorithms for
nonlinear dynamic state estimation. Nonlinearity is considered in two ways: First propagation
is improved by proposing novel methods for approximating continuous probability distributions
by discrete distributions defined on the same continuous domain. Second nonlinear underlying
domains are considered by proposing novel filters that inherently take the underlying geometry
of these domains into account.