The propagation of electromagnetic waves beyond the line of sight can be caused by atmospheric ducts, which are significant concerns in the fields of radar and communication. This paper utilizes data from seven automatic weather stations and five radio-sounding stations to statistically analyze the characteristics of the atmospheric ducts in the northwest region of the South China Sea (SCS). After verifying the practicality of numerical analysis data from NCEP CFSv2 and ECMWF in studying atmospheric ducts using measured data, we analyzed the spatial–temporal distribution characteristics of the height of the regional evaporation duct and the bottom height of the elevated duct. The study found that the NCEP CFSv2 data accurately capture the evaporation duct height and duct occurrence rate in the study area, and the elevated duct bottom height calculated from ERA5 and the measured data have good consistency. The occurrence rate and height of the evaporation duct in coastal stations in the northwest of the SCS vary significantly by month, demonstrating clear monthly distribution patterns; conversely, changes in the Xisha station are minimal, indicating good temporal uniformity. For lower atmospheric ducts, the difference in occurrence rates between 00:00 and 12:00 (UTC) is negligible. The occurrence probability of elevated ducts in the Beibu Gulf area is relatively high, mainly concentrated from January to April, and the Xisha area is dominated by surface ducts without foundation layers, mainly concentrated from June to August. Monsoons play a critical role in the generation and evolution of atmospheric ducts in the northwest of the SCS, with the height of the evaporation duct increasing and the bottom height of the elevated duct decreasing after the onset of the summer monsoon. In the end, we simulated electromagnetic propagation loss under different frequencies and radiation elevation angles in various duct environments within a typical atmospheric duct structure.

As a new type of coherent jamming pattern in modern electronic warfare, Interrupted Sampling Repeater Jamming (ISRJ) has attracted increasing attention in past decades. In this paper, the joint optimization of transmit waveform and receive filter for wideband polarimetric radars is introduced to increase the freedom degrees of radar anti-ISRJ design. The figure of merit is formulated on the basis of the penalty function and Pareto balance framework. Different from off-the-shelf anti-jamming methods employing waveform design, the modulation of Target Impulse Response Matrix (TIRM) to the target echo is considered in the system model, for the sake of matching with a wideband illumination scenario. To solve the non-convex problem, a Majorization-Minimization (MM) based alternate iteration method is proposed. Numerical results validate the effectiveness of the proposed method, and highlight that compared to conventional single-polarization radars, a better anti-ISRJ performance can be achieved by waveform design of polarimetric radars.

Main-lobe interrupted-sampling repeater jamming (ISRJ) poses a serious threat to the normal use of radar. The existing anti-ISRJ target detection methods based on deep learning have problems such as a large number of training samples required, complex use steps, inability to achieve ranging and speed measurement at the same time, and poor detection probability under a very low signal-to-noise ratio (SNR) environment. To solve these problems, this paper proposes an end-to-end anti-ISRJ target detection method based on the range-Doppler spectrum. First, a visual object detection network based on convolutional neural networks (CNN) is designed and trained with fewer than 9000 samples. Focal loss is introduced to train the network. It can adaptively improve the loss weight of hard samples, and therefore improve the performance of the network under a low SNR environment. After that, the range-Doppler spectrum corresponding to the radar echo signal is input into the trained network, and then the detection, ranging, and speed measurement of the targets under ISRJ conditions can be realized. The simulation results show that the detection probability of the proposed method under various SNRs, signal-to-jamming ratios (SJR), and false alarm rates is better than that of the compared method. The proposed method can achieve a detection probability of more than 80% under the condition of a false alarm rate of 1e-4, SNR of -20 dB, and SJR of not less than -20 dB.

The problem of passive localization using an unmanned aerial vehicle (UAV) swarm is studied. For multi-UAV localization systems with limited communication and observation range, the challenge is how to obtain accurate target state consistency estimates through local UAV communication. In this paper, an ant colony pheromone mechanism-based passive localization method using a UAV swarm is proposed. Different from traditional distributed fusion localization algorithms, the proposed method makes use of local interactions among individuals to process the observation data with UAVs, which greatly reduces the cost of the system. First, the UAVs that have detected the radiation source target estimate the rough target position based on the pseudo-linear estimation (PLE). Then, the ant colony pheromone mechanism is introduced to further improve localization accuracy. The ant colony pheromone mechanism consists of two stages: pheromone injection and pheromone transmission. In the pheromone injection mechanism, each UAV uses the maximum likelihood (ML) algorithm with the current observed target bearing information to correct the initial target position estimate. Then, the UAV swarm weights and fuses the target position information between individuals based on the pheromone transmission mechanism. Numerical results demonstrate that the accuracy of the proposed method is better than that of traditional localization algorithms and close to the Cramer–Rao lower bound (CRLB) for small measurement noise.

Azimuth multichannel synthetic aperture radar (SAR) possesses high-resolution and wide-swath (HRWS) imaging capabilities, posing a significant threat to the survivability of critical targets during wartime. However, due to the constraints of multichannel reconstruction processing, existing jamming technology will produce a periodic jamming effect in the azimuth direction when against HRWS-SAR. This unique periodic repetition related to the number of channels will make the jamming easy to detect and eliminate. Meanwhile, the amplitude of the jamming signal suffers from the modulation of sine functions, which causes a waste of jamming energy. Against the above problems, this article introduces a pre-segmentation idea, effectively circumventing the aforementioned problems. Meanwhile, this article proposes a two-dimensional (2-D) precise controllable smart jamming method designed for HRWS-SAR. The proposed method can generate two jamming effects, barrage and deceptive, at designated locations within the targeted area. First, 2-D expand factors are defined respectively to achieve precise control of the barrage jamming range. Then, by controlling multiple jamming parameters, a highly controllable deceptive jamming effect can be achieved. Additionally, a parameterized jamming model is established, which can achieve 2-D precise control of the jamming position through the cooperation of the signal and spatial domain. Notably, this study focuses not only on the impact of different jamming parameters on imaging results but also on evaluating jamming effectiveness under parameter estimation errors to guide optimal jamming strategies. Finally, extensive numerical simulation experiments and comparisons with other methods are conducted to verify the superiority of the proposed method.

With advancements in phased array and cognitive technologies, the adaptability of modern multifunction radars (MFRs) has significantly improved, enabling greater flexibility in waveform parameters and beam scheduling. However, these enhancements have made it increasingly difficult to establish fixed relationships between working modes using traditional radar recognition methods. Furthermore, conventional approaches often exhibit limited robustness and computational efficiency in complex or noisy environments. To address these challenges, this paper proposes a joint learning framework based on a hybrid model combining convolutional neural networks (CNNs) and Transformers for MFR working mode recognition. This hybrid model leverages the local convolution operations of the CNN module to extract local characters from radar pulse sequences, capturing the dynamic patterns of radar waveforms across different modes. Simultaneously, the multi-head attention mechanism in the Transformer module models long-range dependencies within the sequences, capturing the “semantic information” of waveform scheduling intrinsic to MFR behavior. By integrating characters across multiple levels, the hybrid model effectively recognizes MFR working modes. This study used the data of the Mercury MFR for modeling and simulation, and proved through a large number of experiments that the proposed hybrid model can achieve robust and reliable identification of advanced MFR working modes even in complex electromagnetic environments.