With the increasingly complex electromagnetic environment and the intelligent development of radar, the jammer, as opposed to radar, urgently needs to improve its ability to recognize threat targets and make jamming decisions. In this paper, we first introduce the concepts and systems of cognitive electronic warfare (CEW) and summarize its research status. Through analysis of the existing CEW systems, we propose a CEW model suitable for the cluster confrontation scenarios. Then, for the radar jamming decision-making (RJDM) namely a crucial part of CEW, we discuss the advantages, disadvantages, and applications of the traditional methods and analyze the machine-learning based methods including Markov decision processing, the newest Q-Learning, Deep Q-Learning (DQN), Double Deep Q-Learning (DDQN), A3C algorithms, and their improved algorithms etc. We build radar adversarial models and verify the effectiveness of reinforcement learning (RL) algorithm and the superiority of deep RL by simulating both the underlying Q-Learning and DQN algorithms. Finally, the research trends of CEW are discussed.

This letter is devoted to the layout reconstruction via the ultra-wideband (UWB) through-wall imaging radar under one single observation and simultaneously takes account of the real-time human indication. In the proposed framework, layout reconstruction is taken as the preprocessing, where a coherent processing interval consisting of several successive received echoes in the initial stage is first employed to construct a range-Doppler (RD) spectrum. Then, in the RD spectrum, a series of selected discrete Doppler frequency signals is used to form Doppler back projection (BP) images. Finally, in the Doppler BP image stack, we design a 3-D constant false alarm rate detector to extract the building layout. Once completed, the achieved layout as auxiliary information is fused with the simultaneous human indication. Through-wall experiments show that the proposed method can effectively extract the covered layout of multiple walls under one single view and accordingly provide strong support for the concealed human sensing.

Limited by the total length, the total number of the antenna units as well as their topology, the radar images always suffered from the sidelobe/grating lobe which severely impacts the quality of the radar images. In this article, a convolutional neural network (CNN)-based radar image-enhancing method is proposed. Using the original radar images as the input samples and using their corresponding ideal radar images with no sidelobe/grating lobe as the label to train the CNN. A well-trained CNN can suppress the sidelobe/grating lobe in the radar images. The structure of the specific CNN, the generation methods of the samples and the labels, the training procedure of the CNN, as well as some other detailed implementation strategies are specifically illustrated in this article. The proposed method is utilized to suppress the sidelobe/grating lobe in both the simulated and real recorded radar images. Compared to other existing methods, the proposed method is with better sidelobe/grating lobe suppressing performance and better robustness.

The main difficulty of synthetic aperture radar (SAR)-optical image matching or registration lies in the significant heterogeneous characteristics introduced by the different imaging mechanisms between SAR and optical images. Instead of directly using the raw image pair, transforming the pair into a feature domain, where they have homogeneous feature representation, is believed more effective. Inspired by image segmentation, we develop an end-to-end deep learning model for the SAR-optical matching, based on a siamese U-net with a fast Fourier transform (FFT) correlation layer. First, the siamese U-net with sharing weights extracts the feature maps of the SAR and optical images and projects the heterogeneous images into a homogeneous space. Then, the two feature maps are cross-correlated or normalized cross-correlated by the FFT layer and a similarity heatmap is obtained. Finally, the heatmap is send into a softmax2d classifier to determine the best matching, and thus matching is converted into classification. The nonlinear mapping capability of deep learning can well tackle the intensity variation across the different imaging modals; the encoder–decoder architecture with skip connections in the U-net can take full advantage of the global information and simultaneously preserve the local resolution and position information and thus guarantees high accuracy and robustness; besides, the FFT correlation is helpful for the efficiency improvement and training with large image pairs. Experiments show that the proposed method can achieve a pixel-level matching error.

In order to improve the efficiency and adaptability of cognitive radar jamming decision-making, a fusion algorithm (Ant-QL) based on ant colony and Q-Learning is proposed in this paper. The algorithm does not rely on a priori information and enhances adaptability through real-time interactions between the jammer and the target radar. At the same time, it can be applied to single jammer and multiple jammer countermeasure scenarios with high jamming effects. First, traditional Q-Learning and DQN algorithms are discussed, and a radar jamming decision-making model is built for the simulation verification of each algorithm. Then, an improved Q-Learning algorithm is proposed to address the shortcomings of both algorithms. By introducing the pheromone mechanism of ant colony algorithms in Q-Learning and using the $\epsilon$-greedy algorithm to balance the contradictory relationship between exploration and exploitation, the algorithm greatly avoids falling into a local optimum, thus accelerating the convergence speed of the algorithm with good stability and robustness in the convergence process. In order to better adapt to the cluster countermeasure environment in future battlefields, the algorithm and model are extended to cluster cooperative jamming decision-making. We map each jammer in the cluster to an intelligent ant searching for the optimal path, and multiple jammers interact with each other to obtain information. During the process of confrontation, the method greatly improves the convergence speed and stability and reduces the need for hardware and power resources of the jammer. Assuming that the number of jammers is three, the experimental simulation results of the convergence speed of the Ant-QL algorithm improve by 85.4%, 80.56% and 72% compared with the Q-Learning, DQN and improved Q-Learning algorithms, respectively. During the convergence process, the Ant-QL algorithm is very stable and efficient, and the algorithm complexity is low. After the algorithms converge, the average response times of the four algorithms are 6.99 × 10⁻⁴ s, 2.234 × 10⁻³ s, 2.21 × 10⁻⁴ s and 1.7 × 10⁻⁴ s, respectively. The results show that the improved Q-Learning algorithm and Ant-QL algorithm also have more advantages in terms of average response time after convergence.

Image registration is the basis for the joint interpretation of synthetic aperture radar (SAR) and optical images. However, the significant nonlinear radiation difference (NRD) and the geometric imaging model difference render the registration quite challenging. To solve this problem, both traditional and deep learning methods are used to extract structural information with dense descriptions of the images, but they ignore that structural information of the image pair is coupled and often process images separately. In this paper, a deep learning-based registration method with a co-attention matching module (CAMM) for SAR and optical images is proposed, which integrates structural feature maps of the image pair to extract keypoints of a single image. First, joint feature detection and description are carried out densely in both images, for which the features are robust to radiation and geometric variation. Then, a CAMM is used to integrate both images’ structural features and generate the final keypoint feature maps so that the extracted keypoints are more distinctive and repeatable, which is beneficial to global registration. Finally, considering the difference in the imaging mechanism between SAR and optical images, this paper proposes a new sampling strategy that selects positive samples from the ground-truth position’s neighborhood and augments negative samples by randomly sampling distractors in the corresponding image, which makes positive samples more accurate and negative samples more abundant. The experimental results show that the proposed method can significantly improve the accuracy of SAR–optical image registration. Compared to the existing conventional and deep learning methods, the proposed method yields a detector with better repeatability and a descriptor with stronger modality-invariant feature representation.