GAN-Based Radar Spectrogram Augmentation via Diversity Injection Strategy

The classification of human activity using radar has gained considerable attention in recent years because of the radar sensor's resistance to harsh settings. However, when using machine learning algorithms to train a radar-based classifier, a substantial amount of training data is always required, which means that the time-consuming and difficult radar measurements should be a prerequisite activity in most scenarios. The purpose of this article is to present a novel data augmentation methodology for generating sufficient and diverse training data for human activity classification, hence alleviating the reliance on complex radar measurements. It is worth noting that the proposed model only uses one spectrogram as a reference for spectrogram augmentation, which demonstrates its great potential in practical scenarios. Considering the contingent risk of a lack of diversity in augmented samples, we develop an elaborate strategy for injecting diversity into augmented samples using external counterparts. To validate our model, we conduct radar simulations and measurements to create a variety of datasets. Our model outperforms other comparable models, demonstrating its great potential in improving the performance of human activity classification.