Magnetic Anomaly Detection Method with Empirical Mode Decomposition and Minimum Entropy Feature

Document Type : Original Article

Authors

1 PhD student, Imam Hossein University (AS), Tehran, Iran

2 Associate Professor, Imam Hossein University (AS), Tehran, Iran

3 Assistant Professor, Imam Hossein University (AS), Tehran, Iran

Abstract

Abstract: Magnetic anomaly detection (MAD) is a passive method for airborne detection of subsurface objects. Light, radar and sound waves are unable to pass through the open air into the sea water environment and penetrate deep into the water, weaken or return to the air environment. On the other hand, the magnetic field force lines on this boundary are unchanged. MAD method is based on measuring the smallest changes or anomalies caused by the earth's magnetic field due to the passage of a ferro-magnetic object and the magnetic bipolar field generated around it, and especially in shallow waters, is one of the most efficient methods. Due to the rapid reduction of the magnetic field by increasing the distance, the magnetic disturbance generated by the magnetic target in the distance, usually buried in magnetic noise, in other words, the signal-to-noise ratio (SNR) decreases. In this paper, in order to improve the detection performance of magnetic impairment in low SNR, a hybrid method of MAD based on empirical mode decomposition (EMD) and minimal entropy method is proposed. In order to evaluate the performance of the method, an electronic measuring device has been constructed and magnetic data have been fieldly harvested from caspian sea in Anzali port area. These data impregnated with environmental magnetic noise have been investigated by entropy method. According to the entropy feature, magnetic anomaly is detected whenever entropy degrades below the defined threshold. In this way, the proposed method for detecting weak magnetic anomalies is also effective. The test results indicate a high probability of detection of the proposed method for low input SNR. Compared to the original signal SNR with -10 dB, the reconstructed signal SNR has improved to 8 dB. In addition, the total time of updating the parameters of the probability density function (PDF ), of noise is about 0.075s obtained.

Keywords

Smiley face

References

[1]    S. L. Tantum, Y. Yu, and L. M. Collins, “Bayesian mitigation of sensor position errors to improve unexploded ordnance detection,” IEEE Geoscience and Remote Sensing Letters, vol. 5, no. 1, pp. 103–107, 2008.
[2]    Z. Guo, D. Liu, Q. Pan, Y. Zhang, Y. Li, and Z. Wang, “Vertical magnetic field and its analytic signal applicability in oil field underground pipeline detection,” Journal of Geophysics and Engineering, vol. 12, no. 3, pp. 340–350, 2015.
[3]    J. A. Baldoni and B. B. Yellen, “Magnetic tracking system: monitoring heart valve prostheses,” IEEE Transactions on Magnetics, vol. 43, no. 6, pp. 2430–2432, 2007.
[4]    D. Liu, X. Xu, C. Huang et al., “Adaptive cancellation of geomagnetic background noise for magnetic anomaly detection using coherence,” Measurement Science and Technology, vol. 26, no. 1, 2015.
[5]    J. Ge, S. Wang, H. Dong et al., “Real-time detection of moving magnetic target using distributed scalar sensor based on hybrid algorithm of particle swarm optimization and gauss-newton method,” IEEE Sensors Journal, vol. 20, no. 18, pp. 10717–10723, 2020.
[6]    C. Wan, M. Pan, Q. Zhang, D. Chen, H. Pang, and X. Zhu, “Performance improvement of magnetic anomaly detector using karhunen–loeve expansion,” IET Science, Measurement and Technology, vol. 11, no. 5, pp. 600–606, 2017.
[7]    A. Sheinker, N. Salomonski, B. Ginzburg, L. Frumkis, and B.- Z. Kaplan, “Magnetic anomaly detection using entropy filter,” Measurement science and technology, vol. 19, no. 4, 2008.
[8]    A. Sheinker, A. Shkalim, N. Salomonski, B. Ginzburg, L. Frumkis, and B.-Z. Kaplan, “Processing of a scalar magnetometer signal contaminated by 1/fα noise,” Sensors and Actuators A: Physical, vol. 138, no. 1, pp. 105–111, 2007.
[9]    A. Sheinker, B. Ginzburg, N. Salomonski, P. A. Dickstein, L. Frumkis, and B.-Z. Kaplan, “Magnetic anomaly detection using high-order crossing method,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 4, pp. 1095–1103, 2012.
[10] C. Wan, M. Pan, Q. Zhang, F. Wu, L. Pan, and X. Sun, “Magnetic anomaly detection based on stochastic resonance,” Sensors and Actuators A: Physical, vol. 278, pp. 11–17, 2018.
[11] L. Fan, X. Kang, Q. Zheng et al., “A fast linear algorithm for magnetic dipole localization using total magnetic field gradient,” IEEE Sensors Journal, vol. 18, no. 3, pp. 1–1038, 2017.
[12] Y. Tang, Z. Liu, M. Pan et al., “Detection of magnetic anomaly signal based on information entropy of differential signal,” IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 4, pp. 512–516, 2018. 2019.
[13] H. Zhou, Z. Pan, and Z. Zhang, “Magnetic anomaly detection with empirical mode decomposition trend filtering,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E100.A, no. 11, pp. 2503–2506, 2017.
[14] B. Ginzburg, L. Frumkis, and B. Z. Kaplan, “Processing of magnetic scalar gradiometer signals using orthonormalized functions,” Sensors and Actuators A: Physical, vol. 102, no. 1-2, pp. 67–75, 2002.
[15] L. Fan, C. Kang, H. Hu et al., “Gradient signals analysis of scalar magnetic anomaly using orthonormal basis functions,” Measurement Science and Technology, 2020.
[16] A. Sheinker, A. Shkalim, N. Salomonski, B. Ginzburg, L. Frumkis, and B.-Z. Kaplan, “Processing of a scalar magnetometer signal contaminated by 1/f α noise,” Sensors and Actuators A: Physical, vol. 138, no. 1, pp. 105–111, 2007.
[17] S. Liu, J. Hu, P. Li et al., “Magnetic anomaly detection based on full connected neural network,” IEEE Access, vol. 7, pp. 198–206, 2019.
[18] H. Zhao, J. Zheng, W. Deng, and Y. Song, “Semi-supervised broad learning system based on manifold regularization and broad network,” IEEE Transactions on Circuits and Systemst, Regular Papers, vol. 67, no. 3, pp. 983–994, 2020.
[19] W. Deng, H. Liu, J. Xu, H. Zhao, and Y. Song, “An improved quantum-inspired differential evolution algorithm for deep belief network,” IEEE Transactions on Instrumentation and Measurement, 2020.
[20] S. Nalband, A. Prince, and A. Agrawal, “Entropy-based feature extraction and classification of vibro arthographic signal using complete ensemble empirical mode decomposition with adaptive noise,” IET Science, Measurement and Technology, vol. 12, no. 3, pp. 350–359, 2018.
[21] C. Wan, M. Pan, Q. Zhang, D. Chen, H. Pang, and X. Zhu, “Performance improvement of magnetic anomaly detector using Karhunen-Loeve expansion,” IET Science, Measurement & Technology, vol. 11, no. 5, pp. 600–606, 2017.
[22] M. Jafari Moghadam, M. Aghababaei, “Design and Construction Laboratory Sample of a Magnetic Anomaly Detector (MAD), ” Master's thesis, Faculty of Electrical and Electronics, Imam Khomeini University of Marine Sciences, Nowshahr, summer 2015 (in Persian).
Applied Electromagnetics
Volume 11, Issue 1 - Serial Number 26
Serial No. 26, Spring & Summer
June 2023
Pages 107-114

Files

History

  • Receive Date: 12 September 2022
  • Revise Date: 14 January 2023
  • Accept Date: 14 March 2023
  • Publish Date: 22 May 2023

Share

How to cite

Statistics