Open AccessHybrid resampling scheme for particle filter‐based inversion
Author(s) -
Zafar Taimoor,
Mairaj Tariq,
Alam Anzar,
Rasheed Haroon
Publication year - 2020
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2018.5531
Subject(s) - resampling , particle filter , algorithm , inversion (geology) , residual , computer science , monte carlo method , multinomial distribution , mathematical optimization , mathematics , artificial intelligence , statistics , kalman filter , paleontology , biology , structural basin
A novel online hybrid resampling (HR) scheme based on the combination of residual and multinomial resampling schemes is proposed. It can be implemented within the framework of the sequential Monte Carlo‐based inversion algorithm, also known as particle filter (PF). Based upon the degeneracy of each particle, the choice of best resampling scheme among both candidates is made at each instant iteratively. Consequently, the inversion performance of PF improves by reducing the computational complexity of the resampling scheme. The proposed online HR scheme is incorporated here within the framework of the PF‐based inversion scheme once applied on nuclear power plant steam generator non‐destructive testing measurement data. The promising results showcase the efficacy of the technique.