Adaptive Magnetic Anomaly Detection Method with Ensemble Empirical Mode Decomposition and Minimum Entropy Feature
Joint Authors
Wang, Huigang
Hu, Hao
Kang, Chong
Fan, Liming
Zou, Mingliang
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-28
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Due to the fast attenuation of the magnetic field along with the distance, the magnetic anomaly generated by the remote magnetic target is usually buried in the magnetic noise.
In order to improve the performance of magnetic anomaly detection (MAD) with low SNR, we propose an adaptive method of MAD with ensemble empirical mode decomposition (EEMD) and minimum entropy (ME) feature.
The magnetic data is decomposed into the multiple intrinsic modal functions (IMFs) with different scales by EEMD.
According to a defined criterion, the magnetic noise and magnetic signal are reconstructed based on IMFs, respectively.
Entropy feature of reconstructed magnetic signal is extracted based on the probability density function (PDF) of the noise which is updated by the reconstructed magnetic noise.
Compared to the traditional minimum entropy method, the entropy feature extracted by the proposed method is more obvious.
The magnetic anomaly is detected whenever the entropy feature drops below the threshold.
Thus, it is effective for revealing the weak magnetic anomaly by the proposed method.
The measured magnetic noise is used to validate the performance of the proposed method.
The results show that the detection probability of the proposed method is higher with low input SNR.
American Psychological Association (APA)
Fan, Liming& Kang, Chong& Wang, Huigang& Hu, Hao& Zou, Mingliang. 2020. Adaptive Magnetic Anomaly Detection Method with Ensemble Empirical Mode Decomposition and Minimum Entropy Feature. Journal of Sensors،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1190663
Modern Language Association (MLA)
Fan, Liming…[et al.]. Adaptive Magnetic Anomaly Detection Method with Ensemble Empirical Mode Decomposition and Minimum Entropy Feature. Journal of Sensors No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1190663
American Medical Association (AMA)
Fan, Liming& Kang, Chong& Wang, Huigang& Hu, Hao& Zou, Mingliang. Adaptive Magnetic Anomaly Detection Method with Ensemble Empirical Mode Decomposition and Minimum Entropy Feature. Journal of Sensors. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1190663
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190663