Many ferromagnetic substances are naturally magnetized under the influence of the geomagnetic field, and information about the corresponding objects can be obtained based on the analysis of monitoring the magnetic fields generated around magnetic targets. The detection and tracking of magnetic targets is the current research hotspot, and the target monitoring based on magnetic signals as well as widely used in various fields. Currently, the magnetic target localization method is commonly used to obtain the relative position of the target by inversion of the magnetic field gradient tensor matrix. However, this method has a detection blind spot, and even if the final results are corrected by mean filtering, there is still a certain amount of error. Therefore, the correction of the localization results needs to be based on the information of the magnetic field gradient matrix collected at the detection point. This paper uses the magnetic field gradient tensor target localization algorithm as the basis of single-point localization, analyzes its error in detection, and proposes a weighted route fitting method based on magnetic field gradient matrix information for the characteristics of route calculation which is vulnerable to great errors. After simulation experiments, the method can reduce the fluctuation of detection results to a certain extent, and can effectively propose the influence of the extreme error generated by the detection blind area on the positioning results. To investigate the sources of error in target localization, we designed a series of experiments for the magnetic field gradient matrix-based localization method. The location of the detection blind zone is verified by changing the relative position of the magnetic target to the detection point. When the position vector is perpendicular to the magnetic moment vector, the error rate increases significantly. The experimental results are consistent with the theoretical formula, and the higher the accuracy of the magnetic sensor, the smaller its detection blind zone. When the detection point out is in the detection blind area, its magnetic field gradient matrix is a non-full-rank matrix, so the accuracy of target detection is often inversely proportional to the dispersion of its matrix eigenvalues. Therefore, by introducing a weighted fitting method based on the gradient matrix, the detection error can be reduced to a certain extent and the interference caused by abnormal data can be eliminated. Experiments are designed to verify the feasibility of the method. The schematic diagram of the positioning detection experiment is shown in Figure 1 below, where ABCED represents the location of different detection points, and the oval dashed line is its detection range. The magnetic target is set to come out from the coordinates (-0.5m,0m) and advance along a straight line to (0.5m,0m), and the comparison of its route monitoring results is shown in Figure 2 below. Based on the results, it can be concluded that the error-corrected route is closer to the set simulation route.

**References:**

Zhao G, Han Q, Tong X, et al. Adaptive filtering method for magnetic anomaly detection[J]. Journal of Applied Remote Sensing, 2018, 12(2):1-10. Kim K Y, Jeong E, Kim S, et al. Magnetic Anomaly Detection Using Continuous Angle Alignment of Three-Axis Magnetic Signal[J]. IEEE sensors journal, 2019, 19(2):743-750. Zheng J, Fan H, Zhang Q, et al. Magnetic Anomaly Target Recognition Based on SVD and SVMs[J]. IEEE Transactions on Magnetics, 2019, 55(9):1-8. Shen Y, Wang J, Shi J, et al. Interpretation of Signature Waveform Characteristics for Magnetic Anomaly Detection Using Tunneling Magnetoresistive Sensor[J]. Journal of Magnetism and Magnetic Materials, 2019, 484(AUG.):164-171. Huang Y, Wu L H, Sun F. Underwater Continuous Localization Based on Magnetic Dipole Target Using Magnetic Gradient Tensor and Draft Depth[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(1):178-180. Nara T, Ito W. Moore–Penrose generalized inverse of the gradient tensor in Euler's equation for locating a magnetic dipole[J]. Journal of Applied Physics, 2014, 115(17PT.3):3646-3648.

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/c3e0fd30c505b2a24e40c70d1a568e97.jpg)
Fig.1 Schematic diagram of magnetic target movement route monitoring

![](https://assets.underline.io/uploads/markdown_image/1/image/18c7f44678b5fffecd2245e1eb1c5fc9.jpg)
Fig.2 Comparison chart of route fitting correction results

 Magnetic Target Motion Monitoring Based on Weighted Route Fitting

*Session Co-Chair: Dirk Meyners, University of Kiel*

*Session Co-Chair: Pavel Ripka, Ceske vysoke uceni technicke v Praze*

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