Vol. 74

Latest Volume
All Volumes
All Issues

Parabolic Trail OBF in Magnetic Anomaly Detection

By Yao Fan, Xiaojun Liu, and Guangyou Fang
Progress In Electromagnetics Research B, Vol. 74, 23-35, 2017


Magnetic anomaly detection (MAD) is to find hidden ferromagnetic objects, and a hidden object is often described as a magnetostatic dipole. Many detection methods are based on the orthonormal basis functions when the target moves along a straight line relatively to the magnetometer. A new kind of parabolic trail orthonormal basis functions (PTOBF) method is proposed to detect the magnetic target when the trajectory of the target is parabola. The simulation experiment confirms that the proposed method can detect the magnetic anomaly signals in white Gaussian noise when SNR is -15.56 dB. The proposed method is sensitive to the characteristic time and curvature. High detection probability and simple implementation of proposed method make it attractive for the real-time applications.


Yao Fan, Xiaojun Liu, and Guangyou Fang, "Parabolic Trail OBF in Magnetic Anomaly Detection," Progress In Electromagnetics Research B, Vol. 74, 23-35, 2017.


    1. Sheinker, A., et al., "Magnetic anomaly detection using entropy filter," Measurement Science & Technology, 19, 2008.

    2. Sheinker, A., et al., "Magnetic anomaly detection using a three-axis magnetometer," IEEE Transactions on Magnetics, Vol. 45, 160-167, 2009.

    3. Sheinker, A., et al., "Magnetic anomaly detection using high-order crossing method," IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, 1095-1103, 2012.

    4. Sheinker, A., et al., "Localization and magnetic moment estimation of a ferromagnetic target by simulated annealing," Measurement Science & Technology, Vol. 18, 3451-3457, 2007.

    5. Yu, H., S. Feng, and L.-H. Wu, "Synchronous correction of two three-axis magnetometers using FLANN," Sensors and Actuators A — Physical, Vol. 179, 312-318, 2012.

    6. Nie, X., et al., "Energy detection based on undecimated discrete wavelet transform and its application in magnetic anomaly detection," Plos One, Vol. 9, e110829-e110829, 2014.

    7. Nie, X. H., Z. M. Pan, and W. N. Zhang, "Wavelet based noise reduction for magnetic anomaly signal contaminated by 1/f noise," Advanced Materials Research, Vol. 889-890, 776-779, 2014.

    8. Zhou, J. J., C. S. Lin, and Y. C. Huan, "Decreasing noise in magnetic anomaly detection basing on wavelet denoising," Applied Mechanics & Materials, Vol. 368–370, 1860-1863, 2013.

    9. Ke, M., P. Liao, and X. Song, "Real-time data mining in magnetic flux leakage detecting in boiler pipeline," International Conference on Digital Manufacturing & Automation, Vol. 2, 130-133, 2010.

    10. Wang, Y., F. Weihuang, and D. P. Agrawal, "Intrusion detection in Gaussian distributed wireless sensor networks," IEEE International Conference on Mobile Adhoc & Sensor Systems, 313-321, 2009.

    11. Zubaidah, T., et al., "Comprehensive geomagnetic signal processing for successful earthquake prediction,", 212-219, 2013.

    12. Sheinker, A., et al., "Processing of a scalar magnetometer signal contaminated by 1/fα noise," Sensors & Actuators A Physical, Vol. 138, 105-111, 2007.

    13. Ma, J. S., et al., "High sensitive nonlinear modulation magnetoelectric magnetic sensors with a magnetostrictive metglas structure based on bell-shaped geometry," Journal of Magnetism and Magnetic Materials, Vol. 405, 225-230, 2016.

    14. Morag, Y., et al., "Thermodynamic signal-to-noise and channel capacity limits of magnetic induction sensors and communication systems," IEEE Sensors Journal, Vol. 16, 1575-1585, 2016.

    15. Zhang, H. and M.-Y. Xia, "Magnetic anomaly detection for simultaneous moving target and magnetometer," Proceedings of 2014 3rd Asia-Pacific Conference on Antennas and Propagation (APCAP 2014), 884-888, IEEE, 2014.

    16. Kay, S. M., Fundamentals of Statistical Signal Processing: Detection Theory, Printice Hall PTR, 1998.