Due to the advantages of Synthetic Aperture Radar (SAR), the study of Automatic Target Recognition (ATR) has become a hot topic. Deep learning, especially in the case of a Convolutional Neural Network (CNN), works in an end-to-end way and has powerful feature-extracting abilities. Thus, researchers in SAR ATR also seek solutions from deep learning. We review the related algorithms with regard to SAR ATR in this paper. We firstly introduce the commonly used datasets and the evaluation metrics. Then, we introduce the algorithms before deep learning. They are template-matching-, machine-learning- and model-based methods. After that, we introduce mainly the SAR ATR methods in the deep-learning era (after 2017); those methods are the core of the paper. The non-CNNs and CNNs, that is, those used in SAR ATR, are summarized at the beginning. We found that researchers tend to design specialized CNN for SAR ATR. Then, the methods to solve the problem raised by limited samples are reviewed. They are data augmentation, Generative Adversarial Networks (GAN), electromagnetic simulation, transfer learning, few-shot learning, semi-supervised learning, metric leaning and domain knowledge. After that, the imbalance problem, real-time recognition, polarimetric SAR, complex data and adversarial attack are also reviewed. The principles and problems of them are also introduced. Finally, the future directions are conducted. In this part, we point out that the dataset, CNN architecture designing, knowledge-driven, real-time recognition, explainable and adversarial attack should be considered in the future. This paper gives readers a quick overview of the current state of the field.

基于磁梯度张量不变量定位方法, 可以实现对目标的单点实时定位, 且定位目标不限于静止目标, 这一方法目前得到了人们的广泛关注, 但该方法由于存在着椭圆系数导致目标定位误差较大的问题. 针对该问题, 提出了一种基于正六面体磁梯度张量测量系统的单点实时定位改进方法, 该方法通过消除原定位方法中不变量存在的椭圆系数, 从而克服椭圆误差对定位精度的影响. 具体做法是通过求解测量系统中正六面体的六个平面中心点处磁梯度张量的特征值, 并把这些特征值按照一定关系进行组合来消除椭圆系数, 来获得六个平面的新不变量, 再对这些新不变量求其梯度值, 根据这些梯度值对目标进行定位, 这样该定位方法可以有效的克服椭圆误差, 可对目标进行单点实时定位. 对改进定位方法进行了仿真实验分析, 结果表明改进方法可以实现目标的单点实时定位, 定位的平均相对误差较现有方法减少10.9%. 改进方法对所搭载平台的机动性要求较低, 其平台可做直线或曲线运动对目标实现单点实时定位.

The presence of complex electromagnetic noise in the ocean significantly impacts the accuracy of ship shaft-rate electric field signal detection, necessitating the development of an effective denoising method to enhance detection precision. Nevertheless, traditional denoising methods encounter issues like low frequency resolution, challenging threshold configuration, and mode mixing. This study introduces a method that integrates variational mode decomposition (VMD) with multi-window spectral subtraction (MSS). The intrinsic mode functions (IMFs) of noisy signals are extracted using VMD, and the noise components within different IMFs are identified. The spectral features of both signal and noise within different IMFs are leveraged to eliminate noise signals via MSS. Subsequently, the denoised components of IMFs are rearranged to derive the denoised ship shaft-rate electric field signals, achieving noise reduction across various frequency bands. Following validation using simulation signals and empirical data, the noise reduction efficacy of VMD-MSS surpasses that of alternative methods, demonstrating robust performance even at low signal-to-noise ratios. The marine electromagnetic noise is effectively suppressed in the empirical data, while preserving the characteristics of ship’s shaft-rate signals, thereby validating the method’s efficacy and demonstrating its practical engineering value.

To solve the problem of false solutions being present in the inversion of magnetic target parameters on airborne scalar magnetometer and of the inversion method being limited to stationary targets, this paper proposes a hybrid method for estimating both the motion parameters and magnetic moments of single moving magnetic target. The proposed method first measures the magnetic field signals along a cross-crossing flight trajectory, then denoises the measurement signals using a signal extraction algorithm based on orthonormal basis. After that, it builds an optimization model based on magnetic dipole model and the geometric relations between the magnetic target and the magnetometer. To solve the problem of the model easily converging to locally optimal solutions, a hybrid algorithm combining the adaptive weighted particle swarm optimization(AWPSO) algorithm and the Levenberg-Marquardt(LM) algorithm is proposed to determine the target parameters. The hybrid algorithm first uses AWPSO algorithm to produce initial values for the target parameters via iteration, and then the initial values of the parameters are used as the input to determine the precise values of the target parameters by fitting the LM algorithm. Simulation and actual test results show that the proposed method has high positioning accuracy and good robustness. Simulation shows that when Gaussian white noise with a standard deviation of 0.2nT is superimposed, the max relative error of the target parameter estimation is 4.6%. The field test results of a water surface ship show that the average positioning error of the method is 20.9m.