Hybrid particle filtering based on an elitist resampling scheme

Beitrag bei einer Tagung
(Konferenzbeitrag)


Details zur Publikation

Autor(en): Halimeh MM, Hümmer C, Brendel A, Kellermann W
Titel Sammelwerk: 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM)
Jahr der Veröffentlichung: 2018
Seitenbereich: 257-261
ISBN: 2151-870X
Sprache: Englisch


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-Autoren / FAU-Herausgeber

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


Zitierweisen

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: 

Zuletzt aktualisiert 2018-16-10 um 11:23