Local polynomial approximation and intersection of confidence intervals for removing noise of lightning electric field measurements

Lightning electric field (LEF) measurements are aperiodic signals characterized by inherent noise of different sources, i.e., it is not possible to register a noise-free signal. In the last decade, the denoising of LEF measurements has been achieved using some time-frequency representations such as...

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Autores:
Rojas-Cubides, Herbert Enrique
Cortés-Guerrero, Camilo Andrés
Forero-Mejía, María Carolina
Tipo de recurso:
Article of journal
Fecha de publicación:
2018
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/68496
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/68496
http://bdigital.unal.edu.co/69529/
Palabra clave:
62 Ingeniería y operaciones afines / Engineering
electric field
intersection of confidence intervals
lightning flash
local polynomial approximation
noise reduction
aproximación local polinomial
campo eléctrico
intersección de intervalos de confianza
rayos
reducción de ruido
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
Description
Summary:Lightning electric field (LEF) measurements are aperiodic signals characterized by inherent noise of different sources, i.e., it is not possible to register a noise-free signal. In the last decade, the denoising of LEF measurements has been achieved using some time-frequency representations such as short-time Fourier transform (STFT), wavelet transform (WT) and fractional Fourier transform (FRFT) without definitive results. In this paper, a denoising process applied on LEF measurements using the Local Polynomial Approximation (LPA) is proposed. The window size selection is made by combining the LPA with the intersection of confidence intervals (ICI) algorithm. Furthermore, a cross-validation criterion is used to select the optimal value of the threshold parameter in the LPA-ICI denoising method. It is shown that for different signal-to-noise ratio (SNR) values, the proposed method significantly reduces the noise present in the recorded signals. Finally, a discussion about the processed signatures in terms of some lightning electric field temporal features is performed.