A Robust Calibration and Adaptive Multipair of Magnetic Gradient Tensors Localization Method for Magnetic Anomaly Detection

The performance of magnetic anomaly detection (MAD) with the magnetic gradient tensor (MGT) measurement system is severely limited by both the measurement errors and the accuracy of the localization algorithm. However, the robust calibration and localization method against various interferences and noises has not received the deserved attention. This study proposes a joint calibration and localization method with MGT to improve the accuracy of noncooperative target (NCT) detection with environmental magnetic interferences and noise. First, compared to the previous calibration method requiring a uniform magnetic field environment and precise triaxial nonmagnetic turntable, a new calibration method based on multi-invariant constraints is studied, which is insensitive to the magnetic interferences and movement of the array center during the calibration. Then, an adaptive multipair of MGTs localization (AMML) method is proposed for the MAD of NCT. On the one hand, the corresponding multipair of MGTs is chosen adaptively depending on the condition number of MGT, which is far more robust against varying movement directions of NCT compared to previous methods. On the other hand, the AMML method is optimized with the Levenberg–Marquardt (LM) algorithm guided by the prior information of analytical scalar triangulation and ranging (STAR) method, which enhances the efficiency and accuracy of the localization method significantly. The experimental results demonstrate that the AMML method reduces the localization error by 39%, 54.3%, and 55.4% compared to the new STAR method (NSM), the Lv-STAR method (LSM), and the conventional STAR method, respectively.