Structural Design and Parameter Optimization of Magnetic Gradient Tensor Measurement System

Author:

Liu Gaigai1ORCID,Zhang Yingzi1ORCID,Liu Wenyi1

Affiliation:

1. State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China

Abstract

Magnetic anomaly detection (MAD) technology based on the magnetic gradient tensor (MGT) has broad application prospects in fields such as unexploded ordnance detection and mineral exploration. The difference approximation method currently employed in the MGT measurement system introduces measurement errors. Designing reasonable geometric structures and configuring optimal structural parameters can effectively reduce measurement errors. Based on research into differential MGT measurement, this paper proposes three simplified planar MGT measurement structures and provides the differential measurement matrix. The factors that affect the design of the baseline distance of the MGT measurement system are also theoretically analyzed. Then, using the magnetic dipole model, the error analysis of the MGT measurement structures is carried out. The results demonstrate that the planar cross-shaped structure is optimal, with the smallest measurement error, only 3.15 × 10−10 T/m. Furthermore, employing the control variable method, the impact of sensor resolution constraints, noise level, target magnetic moment, and detection distance on the design of the optimal baseline distance of the MGT measurement system is simulated and verified. The results indicate that the smaller the target magnetic moment, the farther the detection distance, the lower the magnetometer resolution, the greater the noise, and the greater the baseline distance required. These conclusions provide reference and guidance for the construction of the MGT measurement system based on triaxial magnetometers.

Funder

the Foundation of State Key Laboratory of Dynamic Measurement Technology

the National Natural Science Foundation of China

the Fundamental Research Program of Shanxi Province

the Innovative Research Group Project of the National Science Foundation of China

Publisher

MDPI AG

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