The recognition of synthetic aperture radar (SAR) jamming has gained significant attention due to its critical role in enhancing radar system performance in complex electromagnetic environments. However, existing research typically addresses a limited number of jamming categories, hindering practical applications that require the simultaneous identification of multiple jamming sources. Moreover, the challenges in feature distribution optimization lead to overlaps in inter-class features and inconsistencies within intra-class features, leading to a decrease in the accuracy of SAR jamming recognition. To tackle these issues, we propose a time-frequency aware hierarchical feature optimization method for SAR jamming recognition. Specifically, we propose a timefrequency aware dual attention mechanism to focus on crucial features in the time-frequency map of SAR jamming signal through parallel attention and unified attention. In addition, we propose a hierarchical jamming feature distribution optimization method that further improves the compactness and discrimination of SAR jamming feature representation. We construct an SAR jamming signal dataset with 50 jamming types to validate the proposed recognition method. The detailed results of ablative experiments and comparative experiments demonstrate the superior performance across different JNR conditions achieved by our method, establishing its efficacy for robust SAR jamming recognition.

Deceptive jamming techniques against Synthetic Aperture Radar (SAR) are crucial for defending against hostile SAR reconnaissance and safeguarding our sensitive targets and regions. The current library of deceptive jamming templates for SAR is severely insufficient. The acquisition of measured and simulated SAR target images, which are key sources for the SAR deceptive jamming template library, can be problematic due to their lengthy acquisition period and high cost. In contrast, emulational optical target images are easier to generate. To address the challenge of quickly translating rich optical target images into SAR target deceptive jamming templates of diverse poses and high fidelity, this paper proposes a SAR target deceptive jamming template generation method based on Optical-to-SAR Cycle-consistent Adversarial Networks (OTSCycGAN). Firstly, this method utilizes deformable convolutional layers to partially replace the vanilla convolutional layers in the generator, enhancing the adaptive extraction ability of geometric transformation features between samples with varying azimuth angles, elevation angles, and target types in the dataset. Furthermore, a convolution kernel attention mechanism is introduced to dynamically adjust the receptive field sizes, increasing sensitivity for various target scales. Finally, we employ a loss function that incorporates Wasserstein GAN-Gradient Penalty (WGAN-GP) loss, hybrid L1-L2 loss, and focal frequency loss to advance the image quality at the pixel level. Our experiments on the constructed Target-Optical/SAR dataset demonstrate that this method brings a significant performance boost in SAR deceptive jamming template generation. The results of the stripmap SAR deceptive jamming, using deceptive jamming templates generated by OTSCycGAN, further confirm their effectiveness.

Wideband interference (WBI) can significantly reduce the image quality and interpretation accuracy of synthetic aperture radar (SAR). To eliminate the negative effects of WBI on SAR, we propose a novel end-to-end data-driven approach to mitigate WBI. Specifically, the WBI is mitigated by an explicit function called WBI mitigation–generative adversarial network (WBIM-GAN), mapping from an input WBI-corrupted echo to its properly WBI-free echo. WBIM-GAN comprises a WBI mitigation network and a target echo discriminative network. The WBI mitigation network incorporates a deep residual network to enhance the performance of WBI mitigation while addressing the issue of gradient saturation in the deeper layers. Simultaneously, the class activation mapping technique fully demonstrates that the WBI mitigation network can localize the WBI region rather than the target echo. By utilizing the PatchGAN architecture, the target echo discriminative network can capture the local texture and statistical features of target echoes, thus improving the effectiveness of WBI mitigation. Before applying the WBIM-GAN, the short-time Fourier transform (STFT) converts SAR echoes into a time–frequency domain (TFD) to better characterize WBI features. Finally, by comparing different WBI mitigation methods applied to several real measured SAR data collected by the Sentinel-1 system, the efficiency and superiority of WBIM-GAN are proved sufficiently.

Synthetic aperture radar (SAR) is susceptible to radio frequency interference (RFI), which becomes especially commonplace in the increasingly complex electromagnetic environments. RFI severely detracts from SAR imaging quality, which hinders image interpretation. Therefore, some RFI mitigation algorithms have been introduced based on the partial features of RFI, but the RFI reconstruction models in these algorithms are rough and can be improved further. This paper proposes two algorithms for accurately modeling the structural properties of RFI and target echo signal (TES). Firstly, an RFI mitigation algorithm joining the low-rank characteristic and dual-sparsity property (LRDS) is proposed. In this algorithm, RFI is treated as a low-rank and sparse matrix, and the sparse matrix assumption is made for TES in the time–frequency (TF) domain. Compared with the traditional low-rank and sparse models, it can achieve better RFI mitigation performance with less signal loss and accelerated algorithm convergence. Secondly, the other RFI mitigation algorithm, named as TFC-LRS, is proposed to further reduce the signal loss. The TF constraint concept, in lieu of the special sparsity, is introduced in this algorithm to describe the structural distribution of RFI because of its aggregation characteristic in the TF spectrogram. Finally, the effectiveness, superiority, and robustness of the proposed algorithms are verified by RFI mitigation experiments on the simulated and measured SAR datasets.

The occurrence of dense interference substantially impairs the imaging performance of Sentinel-1 Synthetic Aperture Radar (SAR), leading to a notable degradation in image quality. Existing interference suppression methods are particularly susceptible to signal loss and imaging defocus, as dense interference and target echoes are heavily coupled in multi-dimensional domains. This paper introduces a deep dense interference suppression network based on a multiple attention mechanism for Sentinel-1 SAR data (AMDIS-Net). It reformulates the dense interference suppression challenge as an end-to-end mapping problem. By mapping the highly coupled dense interference and target echoes to a high-dimensional separable feature space, the AMDIS-Net effectively suppress dense interference. The AMDIS-Net leverage multiple attention mechanism to demonstrate the feature dependence of data between the local and global in frequency domain, thereby effectively mitigating dense interference and minimizing the loss of target echoes. The AMDIS-Net is trained on simulated dense interference datasets, then applied to real dense interference SAR data collected by Sentinel-1. Extensive experimental results illustrate the robust performance of the AMDIS-Net in mitigating dense interference.

The primary objective of jamming strategy optimization is to ensure that a jammer timely finds an effective jamming strategy against the multifunction radar (MFR), thereby ensuring the safety of targets. Deep reinforcement learning (DRL) has been widely applied in solving the problem of jamming strategy optimization. However, the process still faces challenges such as low learning efficiency and a heavy memory burden. Therefore, we propose a fast jamming strategy optimization method with imperfect experience. Firstly, we model the radar countermeasure process as a Markov decision process (MDP), and formulate the jamming reward function by combining the jamming effectiveness and the jammer’s operational intent. Secondly, we design a novel hybrid jamming strategy choice module, which uses imperfect experience to improve the optimization efficiency of jamming strategy. Furthermore, to improve sample efficiency and reduce forgetting caused by small replay buffer, we respectively employ a mixed replay buffer strategy and a knowledge consolidation technique. Finally, extensive experiments demonstrate that under the guidance of imperfect experience, our proposed method achieves faster convergence speed and higher strategy accuracy compared with existing DRL-based methods.