Weak Magnetic Anomaly Signal Extraction Method Based on EEMD and DWT

NING Wenxi; WANG Yanhua; FAN Liming; ZHANG Xiaojun; XIE Zhizhen

doi:10.11993/j.issn.2096-3920.2023-0069

Volume 31 Issue 4

Aug 2023

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Article Navigation > Journal of Unmanned Undersea Systems > 2023 > 31(4): 568-574

NING Wenxi, WANG Yanhua, FAN Liming, ZHANG Xiaojun, XIE Zhizhen. Weak Magnetic Anomaly Signal Extraction Method Based on EEMD and DWT[J]. Journal of Unmanned Undersea Systems, 2023, 31(4): 568-574. doi: 10.11993/j.issn.2096-3920.2023-0069

Citation:

NING Wenxi, WANG Yanhua, FAN Liming, ZHANG Xiaojun, XIE Zhizhen. Weak Magnetic Anomaly Signal Extraction Method Based on EEMD and DWT[J]. Journal of Unmanned Undersea Systems, 2023, 31(4): 568-574. doi: 10.11993/j.issn.2096-3920.2023-0069

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Weak Magnetic Anomaly Signal Extraction Method Based on EEMD and DWT

doi: 10.11993/j.issn.2096-3920.2023-0069

NING Wenxi^{1, 2
,},
WANG Yanhua^{1, 3
,},
FAN Liming^{1, 2, 3
,
,},
ZHANG Xiaojun^4
,,
XIE Zhizhen^1
,

1.
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
2.
Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510300, China
3.
Qingdao Research Institute, Northwestern Polytechnical University, Qingdao 266200, China
4.
School of Physics and Optoelectronic Engineering, Harbin Engineering University, Harbin 150001, China

Received Date: 2023-06-01
Accepted Date: 2023-08-09
Rev Recd Date: 2023-07-08

Available Online: 2023-08-14

Abstract

Abstract

Magnetic anomaly signal contains rich feature information of targets, which is the basis for target localization and identification. However, the magnetic anomaly generated by the target rapidly attenuates with detection distance, making the weak magnetic anomaly signals of distant targets typically buried in magnetic noise. In view of extracting weak magnetic anomaly signals with a low signal-to-noise ratio, the method of weak magnetic anomaly signal extraction based on ensemble empirical mode decomposition(EEMD) and discrete wavelet transform(DWT) was proposed. Firstly, EEMD was used to decompose the weak magnetic anomaly signal into signal-domain magnetic signal and noise-domain magnetic signal. Then, the approximate coefficient of DWT was used to characterize the characteristics of the low-frequency signal and obtain the low-frequency noise-domain magnetic signal. Finally, the signal-domain magnetic signal was combined with the low-frequency noise-domain magnetic signal to obtain the weak magnetic anomaly signal. In order to validate the effectiveness of this method, simulation experiments and field experiments were carried out. The experimental results show that this method can effectively suppress background magnetic noise and extract weak magnetic anomaly signals of targets. This method can also offer effective data for the localization and identification of distant targets.
- weak magnetic anomaly,
- signal extraction,
- ensemble empirical mode decomposition,
- discrete wavelet transform,
- noise suppression

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References(27)

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