Modeling and reduction of the gradient effect of a magnetic sensor for an aeromagnetic compensator

A geomagnetic vector compensation method compatible with nonlinear interferences based on back propagation network and 3D Helmholtz coil

Magnetic interferential compensation plays a vital role in geomagnetic vector measurement applications. Traditional compensation accounts for only the permanent interferences, induced field interferences, and eddy-current interferences. However, nonlinear magnetic interferences are found, which also have a great impact on measurement, and it cannot be fully characterized by a linear compensation model. This paper proposes a new compensation method based on a back propagation neural network, which can reduce the influence of the linear model on compensation accuracy due to its good nonlinear mapping capabilities. The high-quality network training requires representative datasets, yet it is a common problem in the engineering field. To provide adequate data, this paper adopts a 3D Helmholtz coil to restore the magnetic signal of a geomagnetic vector measurement system. A 3D Helmholtz coil is more flexible and practical than the geomagnetic vector measurement system itself when generating abundant data under different postures and applications. Simulations and experiments are both conducted to prove the superiority of the proposed method. According to the experiment, the proposed method can reduce the root mean square errors of north, east, and vertical components and the total intensity from 73.25, 68.54, 70.45, and 101.77 nT to 23.35, 23.58, 27.42, and 29.72 nT, respectively, compared with the traditional method.

An Adaptive Alternating Magnetic Interference Suppression (AAIS) Algorithm for Geomagnetic Vector Measurement

To achieve high-precision vector measurement values in a geomagnetic field, it is necessary to develop methods for overcoming alternating magnetic interference (AMI), which is generated by electrical equipment. This paper proposes the adaptive alternating magnetic interference suppression (AAIS) algorithm. In this algorithm, first, only a triaxial fluxgate sensor measures the magnetic field data. The time-frequency diagram of the total magnetic field is obtained quickly through short-time Fourier transform and wavelet transform. Additionally, the time and frequency of AMI appearance are analyzed. Then, the triaxial adaptive notch filter suppresses the three-component related magnetic interference. Herein, simulation and actual experiments are performed to verify the effectiveness of AAIS. The results indicate that the algorithm can quickly detect the frequencies of AMI from the total magnetic field and adaptively fit their amplitude and phase on the vector magnetic field. Finally, AAIS can suppress the interference effectively. The AAIS algorithm realizes error compensation for the vector measurement values by the total magnetic field, which effectively improves the vector measurement accuracy of the geomagnetic field. We highlight that the AAIS algorithm is effective for AMIs of different frequencies, numbers, and intensities without reference sensors. Our work has practical implications in airborne, vehicle-mounted, and shipborne geomagnetic vector detection.

An Improved Aeromagnetic Compensation Method Robust to Geomagnetic Gradient

Aeromagnetic surveys play an important role in many fields, for example, archaeology, anti-submarine warfare, and geophysical exploration. Being in the geomagnetic field, the aircraft generates a great deal of magnetic interference, resulting in bad performance during detection surveys. Thus, it is necessary and important to perform aeromagnetic compensation in advance. Conventional aeromagnetic compensation methods consider that the geomagnetic gradient is approximately zero after bandpass filtering, bringing about the inaccuracy of compensation coefficients. To address this issue, an improved aeromagnetic compensation method robust to geomagnetic gradient is proposed. In this study, the International Geomagnetic Reference Field (IGRF) model was employed to model the geomagnetic gradient. Then, the estimated geomagnetic gradient was subtracted from the measured data, which improved the accuracy of the compensation equations. Field experiments were conducted to verify the effectiveness of the proposed method. The experimental results show that compared to the traditional method, the compensation performance of the proposed method was improved by 152% to 329%. For the level flight, the standard deviation of residual noise after compensation can be as low as 3.3pT. The results indicate that the proposed method can significantly improve the compensation effect, showing great benefits for weak magnetic anomaly detection.