Accurate real-time magnetic detection is crucial for achieving reliable geomagnetic matching navigation. Compared to traditional magnetic parameters (total field, vector components, and gradients), Magnetic Gradient Tensor (MGT) measurements offer superior spatial resolution, enhanced anti-interference capability, and richer information content. MGT data are typically acquired using fluxgate sensor arrays; however, manufacturing tolerances, processing limitations, and inherent signal conditioning circuit imperfections introduce multiple error sources into the measurement system. To address these challenges, this paper analyzes three primary error sources within the detection system: intrinsic fluxgate sensor errors, inter-sensor misalignment errors within the array, and misalignment errors between the inertial navigation system (INS) and the fluxgate array. A comprehensive error calibration methodology for the MGT detection system is proposed. This method establishes corresponding error calibration models based on the characteristics of each error type, identifies relevant constraint relationships, and determines the solution methods for calibration parameters to achieve final error calibration. To validate the effectiveness of the proposed method, an experimental platform based on a non-magnetic turntable was constructed for error calibration experiments. The results demonstrate that the proposed method: Reduces the standard deviation of the computed total field data from fluxgates from 54.36-175.06 nT to below 2 nT, Decreases the Root Mean Square (RMS) error of triaxial readings between individual fluxgates from 301.96-29.06 nT to below 45 nT, and lowers the standard deviation of coordinate-transformed fluxgate triaxial readings from 2,317.77-3,355.33 nT to below 580 nT.

Wang Minkang. Multi-Source Error Comprehensive Suppression Methods for Magnetic Gradient Tensor Detection System. Academic Journal of Engineering and Technology Science (2025), Vol. 8, Issue 4: 131-141. https://doi.org/10.25236/AJETS.2025.080417.

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