Generative Adversarial Network for Radar Signal Generation

TL;DR

This paper introduces a GAN-based approach to generate realistic radar signals for concealed object detection, addressing data scarcity issues in security applications.

Contribution

It presents a novel GAN model trained on FDTD simulated radar data to produce high-quality, indistinguishable radar signals for different object classes.

Findings

Generated radar signals were indistinguishable from real data by human observers.

The GAN successfully modeled different classes of concealed objects.

The approach enhances data availability for radar-based security systems.

Abstract

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.