Effective tracking of surrounding traffic participants allows for an accurate state estimation as a necessary ingredient for prediction of future behavior and therefore adequate planning of the ego vehicle trajectory. One approach for detecting and tracking surrounding traffic participants is the combination of a learning based object detector with a classical tracking algorithm. Learning based object detectors have been shown to work adequately on lidar and camera data, while learning based object detectors using standard radar data input have proven to be inferior. Recently, with the improvements to radar sensor technology in the form of imaging radars, the object detection performance on radar was greatly improved but is still limited compared to lidar sensors due to the sparsity of the radar point cloud. This presents a unique challenge for the task of multi-object tracking. The tracking algorithm must overcome the limited detection quality while generating consistent tracks. To this end, a comparison between different multi-object tracking methods on imaging radar data is required to investigate its potential for downstream tasks. The work at hand compares multiple approaches and analyzes their limitations when applied to imaging radar data. Furthermore, enhancements to the presented approaches in the form of probabilistic association algorithms are considered for this task.

翻译：对周围交通参与者进行有效跟踪，能够实现精确的状态估计，这是预测其未来行为并进而合理规划自车轨迹的必要前提。检测与跟踪周围交通参与者的一种方法，是将基于学习的目标检测器与经典跟踪算法相结合。基于学习的目标检测器已被证明在激光雷达和相机数据上表现良好，而使用标准雷达数据输入的基于学习的目标检测器则被证明效果较差。近年来，随着成像雷达形式的雷达传感器技术的进步，基于雷达的目标检测性能得到了极大提升，但由于雷达点云的稀疏性，其性能与激光雷达传感器相比仍然有限。这给多目标跟踪任务带来了独特的挑战。跟踪算法必须在检测质量有限的情况下，生成一致的轨迹。为此，需要在成像雷达数据上对不同多目标跟踪方法进行比较，以探究其在下游任务中的应用潜力。本研究比较了多种方法，并分析了它们在应用于成像雷达数据时的局限性。此外，针对此任务，还考虑了以概率关联算法形式对所述方法进行增强。