Samsung Advanced Institute of Technology

Attention mechanisms have been widely integrated with various neural networks to boost
performance. However, when an attention mechanism was applied to a radar ego-velocity estimation
network, the importance of carefully handling the amplitude and phase of complex-valued tensor was
revealed. Therefore, in this study, we present a self-attention mechanism designed to handle complexvalued
tensor in order to capture the rich contextual relationships implied within amplitude and phase.
To exploit the advantages of complex-valued attention (CA), we evaluated its impact while performing egovelocity
estimation tasks based on radar data, whose amplitude and phase are related to the electromagnetic
scattering of the target being observed. Radars are suitable sensors for such tasks as they are capable
of long-range detection and instantaneous velocity measurement under variable weather and lighting
conditions. In particular, we coupled our CA module with complex-valued neural networks, known to
be particularly powerful for handling wave phenomena. The proposed method exhibits robust estimation
performance, regardless of whether the Doppler ambiguity problem occurs and eliminates the dependence
on preprocessing stages, including target detection and static target indication. Furthermore, it achieves
improved stability during training via geometrical constraint regularization, and implicitly allows velocity
conversion between the sensor and vehicle frames, even if the mount information of the sensor was
not provided. Finally, ablation experiments conducted on extensive real-world datasets show noticeable
improvement in estimation performance.