Generative Adversarial Network for Radar Signal Generation
This addresses data scarcity in radar-based security systems, but it is incremental as it applies an existing GAN method to a new domain.
The paper tackled the problem of generating radar signal data for concealed object detection by proposing a Generative Adversarial Network (GAN) trained on simulated data, resulting in generated signals that were indistinguishable from training data by human observers.
A major obstacle in radar based methods for concealed object detection on humans and seamless integration into security and access control system is the difficulty in collecting high quality radar signal data. Generative adversarial networks (GAN) have shown promise in data generation application in the fields of image and audio processing. As such, this paper proposes the design of a GAN for application in radar signal generation. Data collected using the Finite-Difference Time-Domain (FDTD) method on three concealed object classes (no object, large object, and small object) were used as training data to train a GAN to generate radar signal samples for each class. The proposed GAN generated radar signal data which was indistinguishable from the training data by qualitative human observers.