Conventional radar systems are often unable to produce highly accurate results for target classification and identification via linear frequency modulation (LFM) signals. The potential of artificial intelligence, particularly deep learning, has been applied in various fields, which promotes utilizing them in the context of target classification in radar systems. However,
to train deep learning models for this task, large datasets of LFM radar signals are required, which are practically difficult to obtain due to the time, effort, and involved high cost. Therefore, the presented work spots the light on utilizing the recent one-dimensional generative adversarial network (GAN) and Wasserstein GAN (WGAN) models to synthesize a large time-series LFM signal dataset from a reference smaller one. Moreover, the work fairly judges the generated LFM
signals realistic via a decent qualitative and quantitative analysis, unlike other studies which rely solely on qualitative evaluation by human observers. The proposed study outcome reveals the WGAN's efficiency in synthesizing high-quality LFM signals while reducing the training time and resource requirements.