Target magnetic moment orientation estimation method based on full magnetic gradient orthonormal basis function
Source abstract
Abstract
Magnetic anomaly detection (MAD) is an effective method to detect the existence and localization of magnetic targets. Magnetic signal processing technology can extract target signals from complex background noise. However, traditional magnetic signal processing methods cannot greatly improve the signal-to-noise ratio (SNR) while also restoring information concerning the target. This is because the main existing method to calculate a target’s magnetic moment requires a pure target signal. Our research regarding the full magnetic gradient orthonormal basis function (FMG-OBF) addresses the problem of low SNRs for the target magnetic anomaly (TMA) signal. However, this method can only detect the presence of the target and cannot obtain the magnetic moment characteristics of the target. Benefiting from the FMG tensor, which contains large amounts of spatial magnetic field information, this paper is devoted to characterizing TMA in different orientations from the signal energy point of view. We analyze the influences of the target magnetic moment’s variation on the energy components of the TMA signal in various orientations, and further propose a target magnetic moment orientation estimation method. Compared with the traditional signal processing method, the proposed method can estimate the magnetic moment orientation of the target while greatly improving the SNR. Therefore, this method has significant application potential for the classification and identification of weak TMA signals in MAD.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Geophysical and Geoelectrical Methods · Geophysical Methods and Applications · Non-Destructive Testing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Optical and Electronic Information, Huazhong University of Science and Technology , Wuhan 430074, China
References (17)
Show source-provided references
1. IEEE Access · 2019 · 7 · 5262210.1109/access.2019.2908006 ↗
2. IEEE Access · 2020 · 8 · 12710.1109/access.2019.2961373 ↗
3. IEEE Sensors Journal · 2019 · 19 · 74310.1109/jsen.2018.2878890 ↗
4. Geophysical Prospecting · 2019 · 67 · 22610.1111/1365-2478.12721 ↗
5. IEEE Trans. Geosci. Remote Sens. · 2020 · 58 · 594410.1109/tgrs.2020.2973322 ↗
6. Recent Patents Biomed. Eng. · 2009 · 2 · 5810.2174/1874764710902010058 ↗
7. Geophysics · 1985 · 50 · 65610.1190/1.1441940 ↗
8. IEEE Trans. Geosci. Remote Sens. · 2008 · 46 · 373110.1109/tgrs.2008.2000878 ↗
9. IEEE Trans. Geosci. Remote Sens. · 2004 · 42 · 75410.1109/tgrs.2003.819882 ↗
10. Journal of Applied Geophysics · 2019 · 162 · 17410.1016/j.jappgeo.2019.01.007 ↗
11. IEEE Trans. Geosci. Remote Sens. · 2010 · 48 · 367610.1109/tgrs.2010.2048918 ↗
12. Journal of Applied Geophysics · 2012 · 79 · 3810.1016/j.jappgeo.2011.12.014 ↗
13. IEEE Trans. Geosci. Remote Sens. · 2014 · 52 · 465810.1109/tgrs.2013.2283346 ↗
14. IEEE Geoscience and Remote Sensing Letters · 2021 · 19 · 750310410.1109/LGRS.2021.3071133 ↗
15. IEEE Trans. Geosci. Remote Sens. · 2012 · 50 · 109510.1109/tgrs.2011.2164086 ↗
16. Sensors and Actuators A: Physical · 2018 · 278 · 1110.1016/j.sna.2018.05.009 ↗
17. IEEE Sensors Journal · 2020 · 20 · 1292810.1109/jsen.2020.3003680 ↗