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 (e.g., a lidar unit). Feature-based alignment requires the sensors to have overlapping fields of view or necessitates the construction of an environment map. Several existing methods 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, which can be difficult in noisy radar data, we instead 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 axis of translation between the radar pair, without the need for a shared field of view or for specialized structures in the environment. In general, the yaw and 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, prove that the radar-radar system is identifiable, and specify the platform excitation requirements. Through simulations studies and real-world experiments, we establish that our method is more reliable and accurate at estimating the yaw and translation axis than state-of-the-art methods. Finally, we show that the full rigid-body transform can be recovered if relatively coarse information about the rotation rate is available.

翻译：雷达数据融合的准确性依赖于传感器对之间空间变换关系的精确标定。现有方法通过对齐不同雷达扫描中的可识别特征，或依赖其他更精确传感器（如激光雷达）的测量值来确定该变换。基于特征的标定要求传感器具有重叠视场，或需要构建环境地图。部分现有方法甚至需要定制化反射靶标。这些限制条件制约了雷达标定方法的适用场景与操作流程。本文提出一种新方法：不同于在噪声密集的雷达数据中追踪目标或特征的常规思路，我们直接利用各雷达的自速度估计进行标定。该方法无需共享视场或环境中特殊结构，即可实现变换参数子集的标定，包括雷达对之间的偏航角和平移轴。通常而言，偏航角与平移轴是数据融合中最关键的参数，也是最易随时间漂移且最难手动校准的参数。我们将标定问题建模为批量优化问题，证明了雷达-雷达系统的可辨识性，并明确了平台激励要求。通过仿真实验与实测验证，我们证明该方法在偏航角和平移轴估计方面比现有最优方法具有更高的可靠性和精度。最后，当可获取较粗略的旋转速率信息时，本方法能够恢复完整的刚体变换矩阵。