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.

使用基于学习的目标检测器和经典的跟踪算法相结合的方法，对成像雷达数据上的多目标跟踪方法进行了比较和分析，研究其在后续任务中的潜力，并考虑了概率关联算法的改进。