Simulation is an invaluable tool for radio-frequency system designers that enables rapid prototyping of various algorithms for imaging, target detection, classification, and tracking. However, simulating realistic radar scans is a challenging task that requires an accurate model of the scene, radio frequency material properties, and a corresponding radar synthesis function. Rather than specifying these models explicitly, we propose DART - Doppler Aided Radar Tomography, a Neural Radiance Field-inspired method which uses radar-specific physics to create a reflectance and transmittance-based rendering pipeline for range-Doppler images. We then evaluate DART by constructing a custom data collection platform and collecting a novel radar dataset together with accurate position and instantaneous velocity measurements from lidar-based localization. In comparison to state-of-the-art baselines, DART synthesizes superior radar range-Doppler images from novel views across all datasets and additionally can be used to generate high quality tomographic images.

翻译：仿真技术是射频系统设计人员的重要工具，能够快速实现成像、目标检测、分类与跟踪等各类算法的原型验证。然而，模拟逼真的雷达扫描是一项极具挑战的任务，需要精确的场景模型、射频材料特性参数及相应的雷达合成函数。我们提出DART（多普勒辅助雷达断层成像）方法，该方法不直接显式定义上述模型，而是受神经辐射场启发，利用雷达特定物理特性构建基于反射率和透射率的距离-多普勒图像渲染管线。为评估DART性能，我们搭建了定制数据采集平台，基于激光雷达定位系统获取精确位置与瞬时速度测量值，并构建了新型雷达数据集。与当前最优基线方法相比，DART在所有数据集上均能合成更高质量的新视角雷达距离-多普勒图像，同时具备生成高质量断层图像的能力。