IEICE TRANSACTIONS on Fundamentals
The Background Noise Estimation in the ELF Electromagnetic Wave Data Using Outer Product Expansion with Non-linear Filter
Summary :
This paper proposes a background noise estimation method using an outer product expansion with non-linear filters for ELF (extremely low frequency) electromagnetic (EM) waves. We proposed a novel source separation technique that uses a tensor product expansion. This signal separation technique means that the background noise, which is observed in almost all input signals, can be estimated using a tensor product expansion (TPE) where the absolute error (AE) is used as the error function, which is thus known as TPE-AE. TPE-AE has two problems: the first is that the results of TPE-AE are strongly affected by Gaussian random noise, and the second is that the estimated signal varies widely because of the random search. To solve these problems, an outer product expansion based on a modified trimmed mean (MTM) is proposed in this paper. The results show that this novel technique separates the background noise from the signal more accurately than conventional methods.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E97-A No.11 pp.2114-2120
- Publication Date
- 2014/11/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E97.A.2114
- Type of Manuscript
- Special Section PAPER (Special Section on Smart Multimedia & Communication Systems)
- Category
Authors
Akitoshi ITAI
Chubu University
Hiroshi YASUKAWA
Aichi Prefectural University
Ichi TAKUMI
Nagoya Institute of Technology
Masayasu HATA
Chubu University
Keyword
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Akitoshi ITAI, Hiroshi YASUKAWA, Ichi TAKUMI, Masayasu HATA, "The Background Noise Estimation in the ELF Electromagnetic Wave Data Using Outer Product Expansion with Non-linear Filter" in IEICE TRANSACTIONS on Fundamentals,
vol. E97-A, no. 11, pp. 2114-2120, November 2014, doi: 10.1587/transfun.E97.A.2114.
Abstract: This paper proposes a background noise estimation method using an outer product expansion with non-linear filters for ELF (extremely low frequency) electromagnetic (EM) waves. We proposed a novel source separation technique that uses a tensor product expansion. This signal separation technique means that the background noise, which is observed in almost all input signals, can be estimated using a tensor product expansion (TPE) where the absolute error (AE) is used as the error function, which is thus known as TPE-AE. TPE-AE has two problems: the first is that the results of TPE-AE are strongly affected by Gaussian random noise, and the second is that the estimated signal varies widely because of the random search. To solve these problems, an outer product expansion based on a modified trimmed mean (MTM) is proposed in this paper. The results show that this novel technique separates the background noise from the signal more accurately than conventional methods.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E97.A.2114/_p
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@ARTICLE{e97-a_11_2114,
author={Akitoshi ITAI, Hiroshi YASUKAWA, Ichi TAKUMI, Masayasu HATA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={The Background Noise Estimation in the ELF Electromagnetic Wave Data Using Outer Product Expansion with Non-linear Filter},
year={2014},
volume={E97-A},
number={11},
pages={2114-2120},
abstract={This paper proposes a background noise estimation method using an outer product expansion with non-linear filters for ELF (extremely low frequency) electromagnetic (EM) waves. We proposed a novel source separation technique that uses a tensor product expansion. This signal separation technique means that the background noise, which is observed in almost all input signals, can be estimated using a tensor product expansion (TPE) where the absolute error (AE) is used as the error function, which is thus known as TPE-AE. TPE-AE has two problems: the first is that the results of TPE-AE are strongly affected by Gaussian random noise, and the second is that the estimated signal varies widely because of the random search. To solve these problems, an outer product expansion based on a modified trimmed mean (MTM) is proposed in this paper. The results show that this novel technique separates the background noise from the signal more accurately than conventional methods.},
keywords={},
doi={10.1587/transfun.E97.A.2114},
ISSN={1745-1337},
month={November},}
Copy
TY - JOUR
TI - The Background Noise Estimation in the ELF Electromagnetic Wave Data Using Outer Product Expansion with Non-linear Filter
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2114
EP - 2120
AU - Akitoshi ITAI
AU - Hiroshi YASUKAWA
AU - Ichi TAKUMI
AU - Masayasu HATA
PY - 2014
DO - 10.1587/transfun.E97.A.2114
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E97-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2014
AB - This paper proposes a background noise estimation method using an outer product expansion with non-linear filters for ELF (extremely low frequency) electromagnetic (EM) waves. We proposed a novel source separation technique that uses a tensor product expansion. This signal separation technique means that the background noise, which is observed in almost all input signals, can be estimated using a tensor product expansion (TPE) where the absolute error (AE) is used as the error function, which is thus known as TPE-AE. TPE-AE has two problems: the first is that the results of TPE-AE are strongly affected by Gaussian random noise, and the second is that the estimated signal varies widely because of the random search. To solve these problems, an outer product expansion based on a modified trimmed mean (MTM) is proposed in this paper. The results show that this novel technique separates the background noise from the signal more accurately than conventional methods.
ER -
English
