Progressive von Mises-Fisher Filtering Using Isotropic Sample Sets for Nonlinear Hyperspherical Estimation

In this work, we present a novel scheme for nonlinear hyperspherical estimation using the von Mises-Fisher distribution. Deterministic sample sets with an isotropic layout are exploited for the efficient and informative representation of the underlying distribution in a geometrically adaptive manner. The proposed deterministic sampling approach allows manually configurable sample sizes, considerably enhancing the filtering performance under strong nonlinearity. Furthermore, the progressive paradigm is applied to the fusing of measurements of non-identity models in conjunction with the isotropic sample sets. We evaluate the proposed filtering scheme in a nonlinear spherical tracking scenario based on simulations. Numerical results show the evidently superior performance of the proposed scheme over state-of-the-art von Mises-Fisher filters and the particle filter.