Magnetic Multitarget Recognition Based on Magnetic Gradient Tensor

The difficulty in magnetic multitarget recognition is that it is difficult to recognize magnetic targets with different magnetic moments and different depths at the same time. This is because the magnetic moment and depth of a magnetic target affect the strength of the generated magnetic field information, resulting in localization results that favor targets that generate stronger magnetic fields, while targets with weaker magnetic fields cannot be accurately identified. In addition, the traditional Euler equation-based multitarget localization method requires calibration of the sensor array before detection, which requires high calibration accuracy as it relies on the inversion of mathematical equations to solve for magnetic targets. To address this problem, this article proposes an inverse tangent function imaging processing method based on the magnetic gradient tensor (MGT) information depth direction change rate. The method aims to balance the differences in magnetic field information between different magnetic targets and enhance the recognition of the horizontal position of targets with weaker magnetic fields. Based on this, a depth detection method (DDM) based on MGT is proposed to acquire the depth information of magnetic targets. A grid-format data acquisition method is also proposed to obtain the MGT. The method does not require calibration of the sensor array or separation of the magnetic anomaly field from the total field. Simulation and experimental results show that the positioning error of this method for each magnetic target in the x-, y-, and z-axes is less than 0.15 m. For a magnetic target with a depth of 1 m, the x-axis error is 0.15 m, the y-axis error is 0.05 m, and the z-axis error is 0.06 m. For a magnetic target with a depth of 0.7 m, the x-axis error is 0.08 m, the y-axis error is 0.12 m, and the z-axis error is 0.13 m.