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Aunsri N, Pipatphol K, Thikeaw B, et al. A novel adaptive resampling for sequential Bayesian filtering to improve frequency estimation of time-varying signals. Heliyon. 2021;7(4):e06768doi: 10.1016/j.heliyon.2021.e06768.
Aunsri, N., Pipatphol, K., Thikeaw, B., Robroo, S., & Chamnongthai, K. (2021). A novel adaptive resampling for sequential Bayesian filtering to improve frequency estimation of time-varying signals. Heliyon, 7(4), e06768. https://doi.org/10.1016/j.heliyon.2021.e06768
Aunsri, Nattapol, et al. "A novel adaptive resampling for sequential Bayesian filtering to improve frequency estimation of time-varying signals." Heliyon vol. 7,4 (2021): e06768. doi: https://doi.org/10.1016/j.heliyon.2021.e06768
Aunsri N, Pipatphol K, Thikeaw B, Robroo S, Chamnongthai K. A novel adaptive resampling for sequential Bayesian filtering to improve frequency estimation of time-varying signals. Heliyon. 2021 Apr 15;7(4):e06768. doi: 10.1016/j.heliyon.2021.e06768. eCollection 2021 Apr. PMID: 33889786; PMCID: PMC8050938.
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Heliyon. 2021 Apr 15;7(4):e06768. doi: 10.1016/j.heliyon.2021.e06768. eCollection 2021 Apr.

A novel adaptive resampling for sequential Bayesian filtering to improve frequency estimation of time-varying signals.

Heliyon

Nattapol Aunsri, Kunrutai Pipatphol, Benjawan Thikeaw, Satchakorn Robroo, Kosin Chamnongthai

Affiliations

  1. School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand.
  2. Integrated AgriTech Ecosystem Research Unit (IATE), Mae Fah Luang University, Chiang Rai, Thailand.
  3. Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.

PMID: 33889786 PMCID: PMC8050938 DOI: 10.1016/j.heliyon.2021.e06768

Abstract

This paper presents a new algorithm for adaptive resampling, called percentile-based resampling (PBR) in a sequential Bayesian filtering, i.e., particle filter (PF) in particular, to improve tracking quality of the frequency trajectories under noisy environments. Since the conventional resampling scheme used in the PF suffers from computational burden, resulting in less efficiency in terms of computation time and complexity as well as the real time applications of the PF. The strategy to remedy this issue is proposed in this work. After state updating, important high particle weights are used to formulate the pre-set percentile in each sequential iteration to create a new set of high quality particles for the next filtering stage. The number of particles after PBR remains the same as the original. To verify the effectiveness of the proposed method, we first evaluated the performance of the method via numerical examples to a complex and highly nonlinear benchmark system. Then, the proposed method was implemented for frequency estimation for two time-varying signals. From the experimental results, via three measurement metrics, our approach delivered better performance than the others. Frequency estimates obtained by our method were excellent as compared to the conventional resampling method when number of particles were identical. In addition, the computation time of the proposed work was faster than those recent adaptive resampling schemes in literature, emphasizing the superior performance to the existing ones.

© 2021 The Author(s).

Keywords: Adaptive resampling; Bayesian filtering; Computer engineering; Electrical engineering; Frequency estimation; Frequency tracking; Markov chain Monte Carlo; Particle filter; Signal processing

Conflict of interest statement

The authors declare no conflict of interest.

References

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