The main approaches for simulating FMCW radar are based on ray tracing, which is usually computationally intensive and do not account for background noise. This work proposes a faster method for FMCW radar simulation capable of generating synthetic raw radar data using generative adversarial networks (GAN). The code and pre-trained weights are open-source and available on GitHub. This method generates 16 simultaneous chirps, which allows the generated data to be used for the further development of algorithms for processing radar data (filtering and clustering). This can increase the potential for data augmentation, e.g., by generating data in non-existent or safety-critical scenarios that are not reproducible in real life. In this work, the GAN was trained with radar measurements of a motorcycle and used to generate synthetic raw radar data of a motorcycle traveling in a straight line. For generating this data, the distance of the motorcycle and Gaussian noise are used as input to the neural network. The synthetic generated radar chirps were evaluated using the Frechet Inception Distance (FID). Then, the Range-Azimuth (RA) map is calculated twice: first, based on synthetic data using this GAN and, second, based on real data. Based on these RA maps, an algorithm with adaptive threshold and edge detection is used for object detection. The results have shown that the data is realistic in terms of coherent radar reflections of the motorcycle and background noise based on the comparison of chirps, the RA maps and the object detection results. Thus, the proposed method in this work has shown to minimize the simulation-to-reality gap for the generation of radar data.

翻译：针对调频连续波雷达的主要仿真方法基于射线追踪，但这类方法通常计算强度大且未考虑背景噪声。本研究提出一种更快速的调频连续波雷达仿真方法，能够利用生成对抗网络(GAN)生成合成的原始雷达数据。相关代码与预训练权重已在GitHub开源。该方法可生成16个同步线性调频脉冲，使生成数据可应用于雷达数据处理算法（滤波与聚类）的后续开发。这能增强数据增强潜力，例如生成现实中不可复现的非存在或安全关键场景数据。研究中，GAN利用摩托车雷达测量数据训练，用于生成直线行驶摩托车的合成原始雷达数据。生成数据时，摩托车距离与高斯噪声作为神经网络输入。采用弗雷歇初始距离(FID)评估合成的雷达线性调频脉冲。随后，分别基于GAN生成的合成数据与真实数据计算两次距离-方位角(RA)图，并利用自适应阈值与边缘检测算法进行目标检测。结果表明，基于线性调频脉冲对比、RA图分析及目标检测结果，生成的雷达数据在摩托车相干反射与背景噪声方面具有真实性。本方法有效缩小了雷达数据生成的仿真与现实差异。