Magnetic anomaly detection methods for identifying concealed ferromagnetic targets have been widely used in various fields. Enhancing detection capabilities under low signal-to-noise ratios (SNRs) is an urgent concern. To address this challenge, our letter proposes a novel detection method by integrating the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm and the support vector machine (SVM) algorithm. Compared with the empirical mode decomposition (EMD) algorithm, the CEEMDAN algorithm not only adaptively can decompose nonstationary signals but also overcome the problem of modal aliasing. In this detection algorithm, the measured magnetic signal is initially decomposed into various subsignals with distinct time scales by using the CEEMDAN algorithm. Subsequently, 28 formulas are applied to extract time-frequency features from each decomposed subsignal, forming the detection feature set. Then, a detection model is constructed using the SVM algorithm with the feature set derived from the combination of signal and noise data. A series of experiments show that our detection method can improve the detection accuracy probability under the low SNRs. It demonstrates improvements ranging from 2% to 22% under white Gaussian noise with SNRs ranging from −8 to −12 dB. Similarly, under colored noise with SNRs from −2 to −8 dB, improvements ranging from 14% to 46% are achieved. These results highlight the effectiveness of the proposed algorithm in low SNR scenarios.

This paper is concerned with the aeromagnetic compensation, which extracts and subtracts the disturbing magnetic fields generated by the aircraft itself. It is a crucial step in aeromagnetic anomaly target detection. The classical Tolles-Lawson model is established based on many assumptions, including at least the following three points. First, the nonorthogonality, zero offsets and nonlinearity of the triaxial fluxgate are ignorable. Second, the aircraft has no movable part and keeps rigidity during flying. Third, the ambient electric field does not induce current on the aircraft surface to produce extra magnetic interference. By taking account of these factors, enhanced models are derived in this work. Meanwhile, fitting and filtering schemes are introduced to suppress the impacts of geomagnetic gradient and diurnal magnetic variations. Field experimental data are used to examine the enhanced models and algorithms. Compared with the original Tolles-Lawson model, the proposed models can significantly improve the compensation accuracy and signal-to-noise ratio, which facilitates the subsequent detection for a magnetic anomaly target.

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.

The deep learning-based automatic recognition of the scanning or exposing region in medical imaging automation is a promising new technique, which can decrease the heavy workload of the radiographers, optimize imaging workflow and improve image quality. However, there is little related research and practice in X-ray imaging. In this paper, we focus on two key problems in X-ray imaging automation: automatic recognition of the exposure moment and the exposure region. Consequently, we propose an automatic video analysis framework based on the hybrid model, approaching real-time performance. The framework consists of three interdependent components: Body Structure Detection, Motion State Tracing, and Body Modeling. Body Structure Detection disassembles the patient to obtain the corresponding body keypoints and body Bboxes. Combining and analyzing the two different types of body structure representations is to obtain rich spatial location information about the patient body structure. Motion State Tracing focuses on the motion state analysis of the exposure region to recognize the appropriate exposure moment. The exposure region is calculated by Body Modeling when the exposure moment appears. A large-scale dataset for X-ray examination scene is built to validate the performance of the proposed method. Extensive experiments demonstrate the superiority of the proposed method in automatically recognizing the exposure moment and exposure region. This paradigm provides the first method that can enable automatically and accurately recognize the exposure region in X-ray imaging without the help of the radiographer.

The orthogonal basis functions (OBFs) method is a viable approach for magnetic target signal detection under the Gaussian white noise. However, its performance would degrade overtly under the Gaussian color noise with the power spectral density of 1/fα, which is the common environmental magnetic noise. In this letter, based on the linear constrained minimum variance criterion and the generalized likelihood ratio test, respectively, an alternative detection scheme is proposed through the optimization of the basis functions by considering both the signal and noise information. Experiment results using both the simulated data and the measured data show that the present approach can significantly improve the detection performance when compared to the original OBFs method.

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 a4He 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).

This paper presents a modified method to evaluate the electromagnetic field induced by the wake of a moving slender body in the ocean of finite depth. The electric current induced in the moving seawater by cutting the geomagnetic field will produce additional electromagnetic fields. The additional fields consist of two distinct modes: the oscillating surface wake mode and transient volume wake mode. It is found that, for a given submerged depth, the magnitude of induced magnetic field near the ocean surface is of the order of several nano- Teslas, which is little influenced by the depth of ocean.

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.