Hybrid particle filtering based on an elitist resampling scheme
Halimeh MM, Hümmer C, Brendel A, Kellermann W (2018)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2018
Edited Volumes: 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM)
Pages Range: 257-261
Event location: Sheffield
ISBN: 2151-870X
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.
Authors with CRIS profile
Mhd Modar Halimeh
Professur für Signalverarbeitung
Andreas Brendel
Professur für Signalverarbeitung
Walter Kellermann
Professur für Signalverarbeitung
How to cite
APA:
Halimeh, M.M., Hümmer, C., Brendel, A., & Kellermann, W. (2018). Hybrid particle filtering based on an elitist resampling scheme. In Proceedings of the Sensor Array and Multichannel Signal Processing Workshop (SAM) (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.
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