Adaptive genetic algorithms for particle filtering improvement

Name: Chanin Kuptametee

Address: 333 Moo1, Thasud, Muang, Chiang Rai 57100

Organization : Mae Fah Luang University

Email : library@mfu.ac.th

keyword: Particle Degeneracy

MeSH: Genetic algorithms -- 319944

Classification :.DDC: e-thesis

; Particle Diversity

MeSH: Resampling (Statistics) -- 319945

MeSH: Computer simulation -- 316292

MeSH: Computer Engineering -- 316979

MeSH: Frequency response (Dynamics) -- 319946

LCSH: Genetic algorithms -- 319944

LCSH: Resampling (Statistics) -- 319945

LCSH: Computer simulation -- 316292

LCSH: Computer Engineering -- 316979

LCSH: Frequency response (Dynamics) -- 319946

Abstract: Particle filtering is a scheme under sequential Bayesian framework widely employed to estimate state of desired information from the observation data outputted from non-linear, non-Gaussian systems. We proposed an adaptive genetic algorithm-based scheme to enhance quality of the drawn sample vectors of state variables (called particles). Each low-weight parent pairs with a randomly selected high-weight parent. The newly created offspring particle is allowed to replace its low-weight parent only if the weight of the offspring is higher than the weight of the low-weight parent. The accepted offspring particles with high weights can also be paired with the other low-weight parents in order to promote particle diversity. Simulation results show that the new method is superior to state-of-the-art algorithms in estimating one-dimensional and multidimensional state estimation. The new method is also tested in an application under the multiple-model particle filter (MMPF) framework of spectrum and dispersion curve estimation of a time-varying acoustics propagated through an ocean waveguide. The new method still can perform well in capturing the modal frequency. However, the new method is also sensitive to high-intensity time-domain noise where such severe noise causes false frequency contents to be more likely to be misidentified as modal frequency. Such a pilot study of testing the new method on the MMPF indicates that further research and improvements of GAs still be needed.

Mae Fah Luang University. Learning Resources and Educational Media Centre

Address: CHIANG RAI

Email: library@mfu.ac.th

Name: Nattapol Aunsri

Role: Advisor

Created: 2025

Modified: 2025-11-24

Issued: 2025-11-24

วิทยานิพนธ์/Thesis

application/pdf

CallNumber: e-thesis

eng

DegreeName: Doctor of Philosophy

Level: Doctoral Degree

Descipline: Computer Engineering

Grantor: Mae Fah Luang University

©copyrights Mae Fah Luang University

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