Hybrid particle filtering based on an elitist resampling scheme

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Halimeh MM, Hümmer C, Brendel A, Kellermann W
Title edited volumes: 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM)
Publication year: 2018
Pages range: 257-261
ISBN: 2151-870X
Language: English


Abstract

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.


FAU Authors / FAU Editors

Brendel, Andreas
Professur für Nachrichtentechnik
Halimeh, MHD Modar
Lehrstuhl für Digitale Übertragung
Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik


How to cite

APA:
Halimeh, M.M., Hümmer, C., Brendel, A., & Kellermann, W. (2018). Hybrid particle filtering based on an elitist resampling scheme. (pp. 257-261). Sheffield, GB.

MLA:
Halimeh, MHD Modar, et al. "Hybrid particle filtering based on an elitist resampling scheme." Proceedings of the Sensor Array and Multichannel Signal Processing Workshop (SAM), Sheffield 2018. 257-261.

BibTeX: 

Last updated on 2019-09-01 at 09:10