  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.
