Correct radar data fusion depends on knowledge of the spatial transform between sensor pairs. Current methods for determining this transform operate by aligning identifiable features in different radar scans, or by relying on measurements from another, more accurate sensor. Feature-based alignment requires the sensors to have overlapping fields of view or necessitates the construction of an environment map. Several existing techniques require bespoke retroreflective radar targets. These requirements limit both where and how calibration can be performed. In this paper, we take a different approach: instead of attempting to track targets or features, we rely on ego-velocity estimates from each radar to perform calibration. Our method enables calibration of a subset of the transform parameters, including the yaw and the axis of translation between the radar pair, without the need for a shared field of view or for specialized targets. In general, the yaw and the axis of translation are the most important parameters for data fusion, the most likely to vary over time, and the most difficult to calibrate manually. We formulate calibration as a batch optimization problem, show that the radar-radar system is identifiable, and specify the platform excitation requirements. Through simulation studies and real-world experiments, we establish that our method is more reliable and accurate than state-of-the-art methods. Finally, we demonstrate that the full rigid body transform can be recovered if relatively coarse information about the platform rotation rate is available.

翻译：正确的雷达数据融合依赖于传感器对之间空间变换关系的认知。现有方法通常通过对齐不同雷达扫描中的可识别特征，或依赖另一更高精度传感器的测量来确定该变换。基于特征的对齐要求传感器具有重叠视场，或需要构建环境地图。部分现有技术还需定制化逆反射雷达靶标。这些限制制约了标定可执行的范围与场景。本文提出不同方案：不再尝试追踪目标或特征，而是利用各雷达的自速度估计进行标定。本方法能标定变换参数子集（包括雷达对偏航角与平移轴），无需共享视场或专用靶标。一般而言，偏航角与平移轴是数据融合中最关键的参数、最易随时间漂移、且最难手动标定的参数。我们将标定建模为批优化问题，证明雷达-雷达系统的可辨识性，并明确平台激励要求。通过仿真与真实实验，证实本方法比现有最优方法更可靠、更精确。最后，我们证明在获取平台旋转速率粗略信息的前提下，可恢复完整刚体变换。