Hybrid particle filtering based on an elitist resampling scheme

In this paper, the elitist resampling particle filter (ERPF) is
introduced. The ERPF is an approach to combine generic particle filters
based on an evolutionary selection of particles, which introduces a
long-term memory to the selected group of the particles. Thereby, the
ERPF aims at fusing the advantages, e.g., robustness and computational
efficiency, and mitigating the drawbacks of each filter, e.g.,
degeneracy and samples impoverishment. Two variants of the ERPF are
presented in this paper: the discrete ERPF (DERPF) and the continuous
ERPF (CERPF), which to the authors' knowledge represent new forms of
particle filters. The two proposed hybrids are compared to the
corresponding original filters using two well-established benchmark
models to illustrate that the evolutionary combinations provide a better
estimation accuracy and exhibit an increased robustness against
outliers.