Direction-of-arrival (DOA) estimation in complex electromagnetic environments plays a critical role in target detection and localization. However, achieving accurate estimation remains a challenge under adverse and non-ideal conditions. We propose a novel self-supervised learning and adversarial augmentation complex-valued convolutional neural network (SACVCNN) for DOA estimation. The method leverages self-supervised pretraining with adversarial data augmentation to improve the robustness of spatial feature extraction, while the use of complex-valued convolutional layers preserves both amplitude and phase information for more comprehensive signal modeling. Simulation results demonstrate that SACVCNN achieves superior accuracy and robustness compared to model-driven and data-driven methods, especially under low-SNR and limited snapshots.
Experimental results indicate that the adopted augmentation can considerably enlarge the training sample set, which consequently enhances the ranging accuracy, and the ResNet-UNet method effectively accomplishes range estimation and its performance markedly surpasses that of the other models.
Qi-Hai Yao, Zi-Jie Zhao, Jia-Xin Lu et al.· Frontiers in Marine Science· 0 citations
Experimental results show that TransPileSiam consistently improves downstream recognition under limited-label conditions and improves robustness, label efficiency, and cross-domain generalization for practical SEI tasks.
Junwei Peng, Siyang Xu, Jiao Wang et al.· International Conference on...· 0 citations
A generative adversarial network (GAN) based few-shot military target detection is presented, and results demonstrate that the method effectively balances few-shot detection accuracy, robustness in complex environments, and real-time inference requirements on embedded edge devices.
Multimodal change detection (CD), due to its ability to flexibly adapt to data acquired from different types of sensors, has become an important research direction in the field of remote sensing. However, existing methods generally lack feature representations with sufficient generalization capacity, leading to pronoun...
Zhi-Fu Zhu, Xi-Ping Yuan, Shu Gan et al.· IEEE Transactions on Geoscie...· 0 citations
Infrared and visible image fusion (IVIF) integrates complementary multi-modal information, yet existing methods typically overlook deliberate adversarial attacks. To enhance model robustness in adversarial environments, we propose a novel adversarial attack resilient network, called Frequency-Aware and Dynamic Curve Ne...
Peng-Cheng Gao, Sheng-Yue Huang· Journal of King Saud Univers...· 0 citations
Automatic Modulation Classification (AMC) is a challenging task in cognitive radio. With the rapid development of deep learning, its powerful feature extraction capability has shown great advantages in AMC. However, deep learning-based modulation classification models are vulnerable to adversarial attacks due to their...
Yu-Xiang Yao, Yan-Die Yang, Qing-Ling Liu et al.· 2026 IEEE/CIC International...· 0 citations
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