In order to improve the detection performance of magnetic anomaly signal with low signal-to-noise ratio (SNR), we develop an effective method using one-dimensional convolutional neural network (1D CNN) model with multi-feature fusion. In the method, the magnetic signal is processed by Hilbert-Huang transform and discrete wavelet transform to obtain its information as pre-feature in different dimensions. The 1D CNN model with three processing blocks is used to further extract features from pre-features and identified whether the anomaly signal exists or not based on multi-feature fusion. To train the model, the positive sample set is generated by simulated signals and the measured magnetic noise, while the negative sample set is only the measured magnetic noise. Simulation results show that the proposed method has high accuracies in training and test set. A field experiment is conducted to examine the detection performance of proposed method using real data. Results show that the proposed method has good detection performances in low SNR.

In magnetic anomaly detection, it is a prerequisite to master the far-field magnetic moment vector information of the target. It is important to measure the magnetic moment of the target in target location, recognition and degaussing. In the paper, we proposed a method of measuring the magnetic moment vector of the target based on the total geomagnetic field. In this method, a sensor array with the scalar magnetometers was constructed and the quantitative factors of the magnetometer were analyzed. In order to eliminate the time-varying and uneven spatial distribution of geomagnetic total field, a double gradient algorithm was applied to magnetic moment measurement in this method. And a criterion function of determining the magnitude and direction of the target magnetic moment was defined. The proposed method was validated by the theoretical calculation and real experimental data. The results showed that the measured magnetic field data were qualitatively in good agreement with the theoretical curve. For the magnitude of magnetic moment, the average relative deviation was 5.1%. And the average relative deviation of inclination and declination of the moment were 3.2% and 2.5%, respectively. In addition, we attempted to separate the induced magnetic moment and the intrinsic magnetic moment of the target. The proposed method can be used as a reference for the target magnetic location and demagnetization.

With the widespread application of infrared technology in military, security, medical, and other fields, the demand for high-definition infrared images has been increasing. However, the complexity of the noise introduced during the imaging process and high acquisition costs limit the scope of research on super-resolution algorithms for infrared images, particularly when compared to the visible light domain. Furthermore, the lack of high-quality infrared image datasets poses challenges in algorithm design and evaluation. To address these challenges, this paper proposes an optimized super-resolution algorithm for infrared images. Firstly, we construct an infrared image super-resolution dataset, which serves as a robust foundation for algorithm design and rigorous evaluation. Secondly, in the degradation process, we introduce a gate mechanism and random shuffle to enrich the degradation space and more comprehensively simulate the real-world degradation of infrared images. We train an RRDBNet super-resolution generator integrating the aforementioned degradation model. Additionally, we incorporate spatially correlative loss to leverage spatial–structural information, thereby enhancing detail preservation and reconstruction in the super-resolution algorithm. Through experiments and evaluations, our method achieved considerable performance improvements in the infrared image super-resolution task. Compared to traditional methods, our method was able to better restore the details and clarity of infrared images.