Synthetic Range-Doppler Map Generating with WGAN-GP: A Study on Convergence and Image Realism for Radar Data Augmentation

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Abstract
Generating data for radar applications, such as range-Doppler maps, is often expensive and time-consuming, and available datasets are typically small, which limits the effectiveness of deep learning models and can significantly hinder performance in tasks like target classification. In this study, we analyzed the feasibility of using Generative Adversarial Networks (GANs), particularly a WGAN-GP with spectral normalization, to generate synthetic range-Doppler map images and expand data availability. Despite our efforts, the results showed that the WGAN-GP struggled to capture the dataset’s complexity, with the discriminator being excessively stronger than the generator. This imbalance led to low visual quality in the generated images and poor performance in the Geometric Score (GS) and SSIM metrics. These findings highlight the challenges of training GANs in complex scenarios and the need for more robust methodologies for future data augmentation applications in radar systems.

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