Improving radar target recognition based on generative adversarial network
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https://doi.org/10.54939/1859-1043.j.mst.93.2024.12-18Keywords:
Radar dataset; Radar Target Recognition; GAN; Deep Learning; Data AugmentationAbstract
In this article, we propose a generative model based on the adversarial network structure to enhance images for the RAD-DAR multi-target dataset. The results of comparisons and evaluations indicate that the images generated by the proposed method exhibit a high degree of similarity to the original images. The experimental process also demonstrates that a deep neural network model trained on the augmented dataset achieves higher accuracy in multi-target recognition compared to a model trained on the original dataset. The proposed data generation model serves as an effective solution to address the data scarcity issue in multi-target datasets.
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