In regards to electromagnetic warfare application, this work proposes a radar intra-pulse recognition algorithm based on bicubic interpolation Wigner-Ville distribution (WVD). The proposed method is aimed at overcoming the complexity of traditional feature extraction and enhancing the robustness of intelligent intra-pulse recognition systems. Firstly, we convert the WVD matrices of radar intra-pulse signals into square matrices using bicubic interpolation. This enables the intra-pulse recognition algorithm to handle radar signals with varying lengths. Secondly, we use the square matrices directly to train a convolutional neural network (CNN), rather than first saving them as images and then training the network with those images. This is because automatic storage and retrieval of images can sometimes alter the numerical values in the matrices. We enhance the performance of the CNN by utilizing batch normalization and one-hot encoding techniques, and continuously evaluate the CNN during training to save the best performing model. Finally, we use the trained CNN to identify the intercepted radar intra-pulses, and verify the reliability of the proposed method based on radar signals generated by hardware devices. Experimental results demonstrate that the proposed algorithm can recognize radar intra-pulses with varying pulse widths. Furthermore, the proposed algorithm exhibits strong recognition performance even at low signal-to-noise ratio, and has lower time complexity than existing algorithms.

Synthetic aperture radar automatic target recognition (SAR-ATR) models based on deep neural networks (DNNs) are vulnerable to attacks of adversarial examples. Universal adversarial attack algorithms can help evaluate and improve the robustness of the SAR-ATR models and have become a research hotspot. However, current universal adversarial attack algorithms have limitations. First, considering the difficulty in obtaining information on the attacking SAR-ATR models, there is an urgent need to design a universal adversarial attack algorithm under a black-box scenario. Second, given the difficulty of acquiring SAR images, the effectiveness of attacks under small-sample conditions requires improvement. To address these limitations, this study proposed a black-box universal adversarial attack algorithm: Transferable Universal Adversarial Network (TUAN). Based on the idea of the generative adversarial network, we implemented the game of generator and attenuator to improve the transferability of universal adversarial perturbation (UAP). We designed loss functions for the generator and attenuator respectively, which can effectively improve the success rate of black-box attacks and the stealthiness of attacks. Additionally, U-Net was used as a network structure of generator and attenuator to fully learn the distribution of examples, thereby enhancing the attack success rate under small-sample conditions. TUAN attained a higher black-box attack success rate and superior stealthiness than up-to-date UAP algorithms in non-targeted and targeted attacks.

Recently, the unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) has become a highly sought-after topic for its wide applications in target recognition, detection, and tracking. However, SAR automatic target recognition (ATR) models based on deep neural networks (DNN) are suffering from adversarial examples. Generally, non-cooperators rarely disclose any SAR-ATR model information, making adversarial attacks challenging. To tackle this issue, we propose a novel attack method called Transferable Adversarial Network (TAN). It can craft highly transferable adversarial examples in real time and attack SAR-ATR models without any prior knowledge, which is of great significance for real-world black-box attacks. The proposed method improves the transferability via a two-player game, in which we simultaneously train two encoder–decoder models: a generator that crafts malicious samples through a one-step forward mapping from original data, and an attenuator that weakens the effectiveness of malicious samples by capturing the most harmful deformations. Particularly, compared to traditional iterative methods, the encoder–decoder model can one-step map original samples to adversarial examples, thus enabling real-time attacks. Experimental results indicate that our approach achieves state-of-the-art transferability with acceptable adversarial perturbations and minimum time costs compared to existing attack methods, making real-time black-box attacks without any prior knowledge a reality.

Recent studies have proven that synthetic aperture radar (SAR) automatic target recognition (ATR) models based on deep neural networks (DNN) are vulnerable to adversarial examples. However, existing attacks easily fail in the case where adversarial perturbations cannot be fully fed to victim models. We call this situation perturbation offset. Moreover, since background clutter takes up most of the area in SAR images and has low relevance to recognition results, fooling models with global perturbations is quite inefficient. This paper proposes a semi-white-box attack network called Universal Local Adversarial Network (ULAN) to generate universal adversarial perturbations (UAP) for the target regions of SAR images. In the proposed method, we calculate the model’s attention heatmaps through layer-wise relevance propagation (LRP), which is used to locate the target regions of SAR images that have high relevance to recognition results. In particular, we utilize a generator based on U-Net to learn the mapping from noise to UAPs and craft adversarial examples by adding the generated local perturbations to target regions. Experiments indicate that the proposed method effectively prevents perturbation offset and achieves comparable attack performance to conventional global UAPs by perturbing only a quarter or less of SAR image areas.

In future electronic warfare (EW), there will be many unmanned aerial vehicles (UAVs) equipped with electronic support measure (ESM) systems, which will often encounter the challenge of radar emitter identification (REI) with few labeled samples. To address this issue, we propose a novel deep learning network, IRelNet, which could be easily embedded in the computer system of a UAV. This network was designed with channel attention, spatial attention and skip-connect features, and meta-learning technology was applied to solve the REI problem. IRelNet was trained using simulated radar emitter signals and can effectively extract the essential features of samples in a new task, allowing it to accurately predict the class of the emitter to be identified. Furthermore, this work provides a detailed description of how IRelNet embedded in a UAV was applied in the EW scene and verified its effectiveness via experiments. When the signal-to-noise ratio (SNR) was 4 dB, IRelNet achieved an identification accuracy of greater than 90% on the samples in the test task.

Recent studies have proven that synthetic aperture radar (SAR) automatic target recognition (ATR) models based on deep neural networks (DNN) are vulnerable to adversarial examples. However, existing attacks are easily failed in the case where adversarial perturbations cannot be fully fed to victim models. We call this situation perturbation offset. Moreover, since background clutter takes up most of the areas in SAR images and has low relevance to recognition results, fooling models with global perturbations is quite inefficient. This paper proposes a semi-whitebox attack network, called Universal Local Adversarial Network (ULAN), to generate universal adversarial perturbations (UAP) for the target regions of SAR images. In the proposed network, we calculate the model’s attention heatmaps through layer-wise relevance propagation (LRP), which is used to locate the target regions of SAR images that have high relevance to recognition results. In particular, we utilize a generator based on the U-Net to learn the mapping from noise to UAPs and craft adversarial examples by adding the generated local perturbations to target regions. Experiments indicate that the proposed method fundamentally prevents perturbation offset and achieves comparable attack performance to conventional global UAPs by perturbing only a quarter or less of SAR image areas.