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 GB

ISBN: 2151-870X

DOI: 10.1109/SAM.2018.8448400

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.

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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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