Aeromagnetic compensation, elimination of magnetic interferences produced by the aircraft platform, plays a vital role in aeromagnetic applications. Conventional compensation methods are based on the Tolles–Lawson (T–L) model, which accounts for only the manoeuvring interferences. However, magnetic interferences due to other factors, such as currents from electrical systems and movable parts, are found significant in practice. In this study, the authors take these interference factors into consideration, including the strobe light and beacon light, the rudder and elevator. The magnetic interference from constant current is equivalent to the ‘permanent magnetisation’ and has been contained in the T–L model. The interferences due to the strobe and beacon lights are modelled to be proportional to the currents and their temporal variations. The model coefficients can be estimated in advance in a ground experiment. The interferences by the pivoting rudder and elevator are still characterised by the T–L model but in the local rudder or elevator coordinate systems, and the compensated coefficients can be obtained on the ground or in the sky. Onsite experiments both on the ground and in the sky are conducted to illustrate the proposed modelling, and improved compensation results are obtained.

A detection procedure for a moving magnetic dipole target by using at least three scalar magnetometers is proposed in this letter. The magnetometers are deployed on the ground or seafloor, while the target is traveling along a straight line at a constant speed above the ground. First, the range from each magnetometer to the closest point of approach (CPA) on the flight line as well as the instant that the target passes by the CPA is estimated by adopting the orthonormal basis function expansion method. Then, the moving parameters, including the moving direction, speed, and elevation, are found through basic geometry relations. Finally, the magnetic moment is determined by solving a linear system by using the least-squares method. Both simulated and field experimental data are employed to demonstrate the proposed scheme.

In this letter, two convolutional neural network (CNN) models for detection of magnetic anomaly target signals are proposed. One is a 1-D CNN combined with signal feature (SF-CNN1D), and the other is a 2-D CNN based on time–frequency diagrams (TF-CNN2D). To train the models, simulated signals are added to the measured background noise to generate the positive sample set, while the negative sample set is the pure measured noise. Simulation results show that both of the models have satisfying train and test accuracies. A field experiment is conducted to verify the generalization ability of the two CNN models to real data. It is demonstrated that the two CNN models have good detection performances and are some better than the conventional support vector machine (SVM) approach by several percent in colored Gaussian noise scenarios.

Aeromagnetic surveys play an important role in geophysical exploration and many other fields. In many applications, magnetometers are installed aboard an aircraft to survey large areas. Due to its composition, an aircraft has its own magnetic field, which degrades the reliability of the measurements, and thus a technique (named aeromagnetic compensation) that reduces the magnetic interference field effect is required. Commonly, based on the Tolles–Lawson model, this issue is solved as a linear regression problem. However, multicollinearity, which refers to the case when more than two model variables are highly linearly related, creates accuracy problems when estimating the model coefficients. The analysis in this study indicates that the variables that cause multicollinearity are related to the flight heading. To take this point into account, a multimodel compensation method is proposed. By selecting the variables that contribute less to the multicollinearity, different sub-models are built to describe the magnetic interference of the aircraft when flying in different orientations. This method restricts the impact of multicollinearity and improves the reliability of the measurements. Compared with the existing methods, the proposed method reduces the interference field more effectively, which is verified by a set of airborne tests.

This paper presents the procedure for evaluating the electromagnetic field induced by the wake of an undersea-moving slender body. The slender body is considered equivalent to a pair of Havelock point sources that can generate wake flow fields. The electric current generated in the seawater is calculated by means of the moving conductive seawater that cuts the earth's magnetic field. The electromagnetic fields in the air and in the seawater are then determined by solving the Maxwell equations with appropriate boundary conditions. The induced electromagnetic fields include two parts: One is the attenuating oscillation that can be ascribed to the free surface Kelvin wake, and the other is the bipolar pulse that can be ascribed to the localized volume wake. Near the sea surface, the magnitude of the former is typically a few hundred picotesla, while that of the latter could be greater by ten times.

Scalar atomic magnetometer (SAM) with high sensitivity is designed to measure the total magnetic field and has been widely applied in aeromagnetic surveys. However, the measured total magnetic field of SAM is influenced by the magnetic disturbances due to the permanent, induced and eddy-current fields of the aircraft and heading errors of SAM. To reduce the magnetic disturbances caused by the aircraft maneuvers, a model was proposed by Tolles and Lawson and has been used in aeromagnetic compensation. However, the heading error of SAM deviates the compensation coefficients estimated by the Tolles-Lawson model. In this letter, we propose a linear model of heading error for multi-cell SAMs and analyze the new aeromagnetic compensation model in which the geomagnetic gradient and heading error are both taken into account. With using a ⁴He magnetometer consisting of three orthogonal cells, the experimental results show that our proposed model has better performances than the previous model in terms of the figure of merit (FOM) and standard deviation (STD).