When using the technique of magnetic gradient tensor measurements to obtain the position of magnetic objects, calibration of the magnetic tensor gradiometer plays a pivotal role in precisely locating the target, and extensive research has been carried out on this up to now. However, previous studies have always lacked sufficient discussion on the position error of magnetometers in magnetic tensor gradiometers caused by inaccurate installment of magnetometers. In this paper, we analyze and correct this position error based on a magnetic dipole source. The result of the simulation exemplifies that the magnetometer’s position error will affect the locating accuracy and, therefore, it is worth correcting this error. The relationship between position error and magnetic gradient tensor components is established, followed by an error correction method based on this relationship. Simulations illustrate that this method can effectively decrease the effect caused by the position error of magnetometers and improve the locating performance with locating error and magnetic moment errors dropping from 2 to 0.2 m and $6\times {10}^5\mathrm{A}\cdot {\mathrm{m}}^2$ to $5\times {10}^4\mathrm{A}\cdot {\mathrm{m}}^2$ , respectively.

agnetic anomaly detection (MAD) using vector magnetometers is extensively agnetic anomaly detection (MAD) using vector magnetometers is extensively M applied in ship detection, vehicle tracking, oil pipeline localization, and unexploded ordnance (UXO) detection. However, inherent magnetometer errors and platform-induced errors significantly limit detection performance. Interference compensation aims to mitigate these effects and enhance detection accuracy. Insufficient compensation data often leads to divergence issues. To address this, we propose an online compensation method employing recursive singular value decomposition (RSVD). This approach provides robust parameter estimation, effectively mitigates online interference even with limited data, and automatically reduces the number of effective singular values to enhance compensation accuracy. Through three experiments, we validate the algorithm’s effectiveness. The RSVD-based compensation method demonstrates excellent magnetic interference suppression and target detection performance under both sufficient and insufficient data conditions. In an offshore test, the peak-to-peak interference value before compensation was 285.6 nT, and compensation using recursive least squares (RLS) failed. After compensation, the interference values using the Kalman and RSVD methods were 9.3 nT and 4.6 nT, respectively. Furthermore, previously undetectable MAD signals became discernible after compensation, with peak-to-peak values of 4.8 nT (Kalman) and 70.9 nT (RSVD). The RSVD-based compensation method performs significantly better in MAD experiments.

The magnetic field characteristics play a crucial role in ship targeting, and magnetic anomaly detection is widely employed for this purpose. Among the various applications, the utilization of sea surface buoys stands out. However, these buoys experience vigorous swaying due to sea wave fluctuations, resulting in substantial noise and a low signal-to-noise ratio (SNR) in the collected data. Consequently, the performance of magnetic anomaly detection suffers, hindering effective identification of the magnetic field signal from the target. To mitigate this challenge, we propose an adaptive method based on variable-step-size normalized least mean square (NLMS) with three magnetic field components as inputs. This method demonstrates rapid convergence, robust stability, real-time noise reduction caused by buoy platform motion, and independence from platform attitude information. Notably, it effectively suppresses the total magnetic field noise induced by the swaying of shallow-sea buoy platforms, thereby enhancing the SNR of the acquired signals. Experimental results reveal that our approach can highlight the magnetic flux curve of naval targets originally submerged in noise, achieving an adaptive gain of 44.69 dB. Ultimately, this method addresses the performance degradation issue in magnetic anomaly detection devices arising from noise interference due to buoy platform oscillations.

Due to the frequent occurrence of blowout accidents and the complex underground environment, the rapid and efficient control of blowout accidents is increasingly emphasized. Relief-Wells technology is one of the most effective methods to address blowout accidents. However, in terms of target well direction determination, there exist issues such as low directional accuracy and significant errors. It is introduced in the paper of a new direction method with two sensors based on Relief-Wells technology. By simultaneously using field information from two fixed-position points, the direction from the blowout well to the relief well could be determined, which is verified by the 3D simulation model with a direction error less than 1%. Furthermore, on-site experiments show an average error of 6.08%. The validity of the proposed method is recognized with these two results, thereby laying the foundation for the rapid sealing of blowout wells.

Currently, electromagnetic scattering from various targets is widely utilized in high-frequency domains, particularly for aerial and terrestrial targets such as radar detection, nondestructive testing, communication, and remote sensing. However, the application of low-frequency electromagnetic scattering insubsurface and underwater environments remains relatively limited, despite its potential to detect unexploded ordnance, underwater objects, and buried seabed metals. Traditional methods for electromagnetic scattering computation often approximate the target as one or more dipoles and other auxiliary sources. While these methods offer a fast computational speed, they tend to introduce relatively large errors. To address this issue, this work employs the Method of Moments (MoM), a highly accurate numerical method, to model and simulate the low-frequency active electromagnetic scattering characteristics of underwater complex metal targets. We selected a typical large underwater unmanned vehicle (UUV) as the metal target for case analysis. The large UUV's comprehensive complex geometry is preserved through detailed CAD modeling and meshing. We simulated the scattering characteristics of the UUV under varying parameters using MoM and conducted the experimental verifications in an open lake environment. Comparisons between simulation and experimental results indicate a high degree of computational accuracy, validating the proposed approach for practical engineering applications.