Estimating parameters of nonlinear systems using the elitist particle filter based on evolutionary strategies

Journal article


Publication Details

Author(s): Hümmer C, Hofmann C, Maas R, Kellermann W
Journal: IEEE/ACM Transactions on Audio, Speech and Language Processing
Publication year: 2018
Volume: 26
Journal issue: 3
Pages range: 595-608
ISSN: 2329-9290
eISSN: 2329-9304
Language: English


Abstract

I this paper, we present the elitist particle filter based on evolutionary strategies (EPFES) as an efficient approach to estimate the statistics of a latent state vector capturing the relevant information of a nonlinear system. Similar to classical particle filtering, the EPFES consists of a set of particles and respective weights which represent different realizations of the latent state vector and their likelihood of being the solution of the optimization problem. As main innovation, the EPFES includes an evolutionary elitist-particle selection scheme which combines long-term information with instantaneous sampling from an approximated continuous posterior distribution. In this paper, we propose two advancements of the previously published elitist-particle selection process. Further, the EPFES is shown to be a generalization of the widely-used Gaussian particle filter and thus evaluated with respect to the latter: First, we consider the univariate nonstationary growth model with time-variant latent state variable to evaluate the tracking capabilities of the EPFES for instantaneously calculated particle weights. This is followed by addressing the problem of single-channel nonlinear acoustic echo cancellation as a challenging benchmark task for identifying an unknown system of large search space: the nonlinear acoustic echo path is modeled by a cas cade of a parameterized preprocessor (to model the loudspeaker signal distortions) and a linear FIR filter (to model the sound wave propagation and the microphone). By using long-term information, we highlight the efficacy of the well-generalizing EPFES in estimating the preprocessor parameters for a simulated scenario and a real smartphone recording. Finally, we illustrate similarities between the EPFES and evolutionary algorithms to outline future improvements by fusing the achievements of both fields of research.


FAU Authors / FAU Editors

Hofmann, Christian
Professur für Nachrichtentechnik
Hümmer, Christian
Professur für Nachrichtentechnik
Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik
Maas, Roland
Professur für Nachrichtentechnik


How to cite

APA:
Hümmer, C., Hofmann, C., Maas, R., & Kellermann, W. (2018). Estimating parameters of nonlinear systems using the elitist particle filter based on evolutionary strategies. IEEE/ACM Transactions on Audio, Speech and Language Processing, 26(3), 595-608.

MLA:
Hümmer, Christian, et al. "Estimating parameters of nonlinear systems using the elitist particle filter based on evolutionary strategies." IEEE/ACM Transactions on Audio, Speech and Language Processing 26.3 (2018): 595-608.

BibTeX: 

Last updated on 2018-16-10 at 11:23