Generation of Synthetic Micro-Doppler Radar Data Using Generative Diffusion Models

International Electrical Engineering Congress (iEECON), pp. 1–6

Abstract

This study investigates the generation of micro-Doppler radar data of cyclists using advanced generative models, namely a Denoising Diffusion Probabilistic Model (DDPM) and a Generative Adversarial Network (GAN). The main objective is to synthesize realistic raw radar data that can effectively mimic real-world scenarios including noise and address the significant challenge of dataset availability in radar technology due to high costs and difficulties in data collection. Radar data collected with a 77GHz automotive radar along the track of cyclists is used to evaluate and compare the performance of a DDPM and a GAN in generating complex-valued raw radar data. Both generative models demonstrated outstanding performance in generating realistic radar data. The DDPM shows a smoother convergence, less variation in loss, and better alignment with the statistical features of the original data compared to the GAN model.

Authors 4

1. Thiri Kay Thwe Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Aachen,Germany

2. Simon Wagner

   Fraunhofer Institute for High Frequency Physics and Radar Techniques

   Affiliation as printed

   Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR,Germany

3. Dirk Heberling Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Aachen,Germany

4. Suramate Chalermwisutkul Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Aachen,Germany