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- W4381745265 abstract "Traditional clutter suppression and target detection methods have limitations in that they must satisfy specific statistical models. In this paper, we propose a unified deep learning model for complex-valued self-attention generative adversarial networks (CV-SAGAN) for clutter suppression and target detection. In the complex-valued framework, we first use a generator module to learn the clutter distribution and perform clutter suppression. Then, a self-attention module is used for the first time to perform corrective detection of sparse targets. Finally, a discriminator is used to judge between the real data and the network output results, improving the robustness of the model. We verified that the CV-SAGAN model has a better detection rate and robustness than the conventional cell-average constant false alarm rate (CA-CFAR), real-valued GAN, and real-valued SAGAN on three clutter distributions and achieved better detection results on the publicly available IPIX real-world dataset." @default.
- W4381745265 created "2023-06-24" @default.
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- W4381745265 date "2023-05-01" @default.
- W4381745265 modified "2023-09-26" @default.
- W4381745265 title "CV-SAGAN: Complex-valued Self-attention GAN on Radar Clutter Suppression and Target Detection" @default.
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- W4381745265 doi "https://doi.org/10.1109/radarconf2351548.2023.10149701" @default.
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