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Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors

Because of the limitations of the traditional fractal box-counting dimension algorithm in subtle feature extraction of radiation source signals, a dual improved generalized fractal box-counting dimension eigenvector algorithm is proposed. First, the radiation source signal was preprocessed, and a Hi...

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Detalles Bibliográficos
Autores principales: Chen, Xiang, Li, Jingchao, Han, Hui, Ying, Yulong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5990805/
https://www.ncbi.nlm.nih.gov/pubmed/29892447
http://dx.doi.org/10.1098/rsos.180087
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author Chen, Xiang
Li, Jingchao
Han, Hui
Ying, Yulong
author_facet Chen, Xiang
Li, Jingchao
Han, Hui
Ying, Yulong
author_sort Chen, Xiang
collection PubMed
description Because of the limitations of the traditional fractal box-counting dimension algorithm in subtle feature extraction of radiation source signals, a dual improved generalized fractal box-counting dimension eigenvector algorithm is proposed. First, the radiation source signal was preprocessed, and a Hilbert transform was performed to obtain the instantaneous amplitude of the signal. Then, the improved fractal box-counting dimension of the signal instantaneous amplitude was extracted as the first eigenvector. At the same time, the improved fractal box-counting dimension of the signal without the Hilbert transform was extracted as the second eigenvector. Finally, the dual improved fractal box-counting dimension eigenvectors formed the multi-dimensional eigenvectors as signal subtle features, which were used for radiation source signal recognition by the grey relation algorithm. The experimental results show that, compared with the traditional fractal box-counting dimension algorithm and the single improved fractal box-counting dimension algorithm, the proposed dual improved fractal box-counting dimension algorithm can better extract the signal subtle distribution characteristics under different reconstruction phase space, and has a better recognition effect with good real-time performance.
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spelling pubmed-59908052018-06-11 Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors Chen, Xiang Li, Jingchao Han, Hui Ying, Yulong R Soc Open Sci Computer Science Because of the limitations of the traditional fractal box-counting dimension algorithm in subtle feature extraction of radiation source signals, a dual improved generalized fractal box-counting dimension eigenvector algorithm is proposed. First, the radiation source signal was preprocessed, and a Hilbert transform was performed to obtain the instantaneous amplitude of the signal. Then, the improved fractal box-counting dimension of the signal instantaneous amplitude was extracted as the first eigenvector. At the same time, the improved fractal box-counting dimension of the signal without the Hilbert transform was extracted as the second eigenvector. Finally, the dual improved fractal box-counting dimension eigenvectors formed the multi-dimensional eigenvectors as signal subtle features, which were used for radiation source signal recognition by the grey relation algorithm. The experimental results show that, compared with the traditional fractal box-counting dimension algorithm and the single improved fractal box-counting dimension algorithm, the proposed dual improved fractal box-counting dimension algorithm can better extract the signal subtle distribution characteristics under different reconstruction phase space, and has a better recognition effect with good real-time performance. The Royal Society Publishing 2018-05-02 /pmc/articles/PMC5990805/ /pubmed/29892447 http://dx.doi.org/10.1098/rsos.180087 Text en © 2018 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Computer Science
Chen, Xiang
Li, Jingchao
Han, Hui
Ying, Yulong
Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors
title Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors
title_full Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors
title_fullStr Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors
title_full_unstemmed Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors
title_short Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors
title_sort improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors
topic Computer Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5990805/
https://www.ncbi.nlm.nih.gov/pubmed/29892447
http://dx.doi.org/10.1098/rsos.180087
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