Analysis of Aeromagnetic Swing Noise and Corresponding Compensation Method

Zhang, Diankun; Liu, Xiaojun; Qu, Xiaodong; Zhu, Wenhao; Huang, Ling; Fang, Guangyou

doi:10.1109/tgrs.2021.3095564

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…where b ij , i = 1, 2, 3; j = 1, 2, 3 represent the eddy field action coefficients. So, a compensation model with 18 coefficients is established [22]:…”

Section: Related Work 21 T-l Modelmentioning

confidence: 99%

Magnetic Interference Analysis and Compensation Method of Airborne Electronic Equipment in an Unmanned Aerial Vehicle

et al. 2023

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At present, the research and application of aeromagnetic compensation are almost all based on the Tolles–Lawson (T–L) model. With the development of unmanned aerial vehicles (UAVs), the number of intelligent electronic devices in UAVs is increasing, and the magnetic environment of the platform is becoming more and more complicated. Research shows that the magnetic interference caused by airborne electronic equipment has been very significant, sometimes even reaching 100 nT. The traditional airborne magnetic compensation method based on the T–L model cannot effectively compensate the magnetic interference caused by airborne electronic equipment. Aiming at the problem of magnetic interference of airborne electronic equipment of UAVs, this paper analyzes the origin of magnetic interference of airborne electronic equipment using experiments, and it was found that it is related to the power supply current, and the characteristics of magnetic interference are similar to permanent magnet materials. Based on this feature, we eliminated the magnetic interference caused by the working current of airborne equipment by establishing a linear compensation model based on the current’s source. The experimental data show that the current interference source model proposed in this paper can effectively compensate the magnetic interference generated by airborne electronic equipment and the compensation improvement ratio (IR) is greater than 10.

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“…where b ij , i = 1, 2, 3; j = 1, 2, 3 represent the eddy field action coefficients. So, a compensation model with 18 coefficients is established [22]:…”

Section: Related Work 21 T-l Modelmentioning

confidence: 99%

Magnetic Interference Analysis and Compensation Method of Airborne Electronic Equipment in an Unmanned Aerial Vehicle

et al. 2023

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show abstract

“…Xu [17] established a binary classification network for magnetic anomaly detection and a regression network for geomagnetic noise suppression and applied deep learning to magnetic anomaly detection and noise elimination. Zhang [18] analyzed the sway interference of aircraft not considered in the T-L model and used a one-dimensional convolutional neural network to eliminate the influence of tail beam sway. Zhou [19] conducted numerical simulation experiments on magnetic interference in aeromagnetic data based on unmanned aerial vehicles and, for the first time, proposed the radial basis function (RBF) artificial neural network (ANN) algorithm to compensate for aeromagnetic data.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Modeling-Based Aeromagnetic Maneuver Noise Suppression Method and Its Application in Mine Detection

Bi,

Yu,

Jiao

et al. 2023

Remote Sensing

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Aeromagnetic measurement plays an important role in mineral exploration, but unmanned aerial vehicles generate maneuvering noise during aerial flight, which negatively impacts the accuracy of aeromagnetic measurement data. Therefore, aeromagnetic compensation is an indispensable step in aeromagnetic data processing. The multicollinearity of variables in the aeromagnetic compensation model based on linear regression affects its accuracy, resulting in a large difference in the compensation effect of the same group of compensation coefficients in different directions. In order to obtain high-quality aeromagnetic data, this study proposes an adaptive model-based method for suppressing aeromagnetic maneuvering noise. First, due to the fact that the variables that cause multiple collinearity in the compensation model are related to the flight heading, the model variables are adaptively assigned to each heading based on the characteristics of the variable data for different headings. The compensation model is optimized and improved, and the impact of multiple collinearity is thus suppressed. In adaptive modeling, variables with greater significance and smaller multicollinearity are automatically allocated to build the optimal heading model, and then high-precision compensation coefficients are obtained. This algorithm was applied to the data collected by a certain unmanned aerial vehicle aeromagnetic measurement platform in Ma’anshan and compared with traditional methods. The experimental results show that the adaptive modeling-based aeromagnetic compensation algorithm is superior to traditional algorithms, with fewer errors and a higher improvement ratio. Hence, the method can effectively solve the ill-conditioned problem of a model affected by multicollinearity and further improve its compensation accuracy and robustness. Moreover, the feasibility and value of this algorithm were verified in actual mineral resource detection.

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“…The first is placing the magnetometer far enough from the platform magnetic source [ 13 ], which belongs to hardware compensation. Mounting methods include support bar [ 14 , 15 , 16 , 17 , 18 , 19 ] and rope [ 20 , 21 ]. The distance of the magnetometer from the noise source varies with the type of carrier, with a typical reference value of 3 m given by Walter and Braun [ 13 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

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

Wang¹,

Li²,

Yang³

et al. 2022

Sensors

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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.

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Analysis of Aeromagnetic Swing Noise and Corresponding Compensation Method

Cited by 12 publications

References 17 publications

Magnetic Interference Analysis and Compensation Method of Airborne Electronic Equipment in an Unmanned Aerial Vehicle

Magnetic Interference Analysis and Compensation Method of Airborne Electronic Equipment in an Unmanned Aerial Vehicle

An Adaptive Modeling-Based Aeromagnetic Maneuver Noise Suppression Method and Its Application in Mine Detection

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

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