用频域处理雷达数据，提升高速移动下的多目标跟踪鲁棒性。
Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain
- 在频域处理雷达信号，增强抗噪和结构误差能力。
- 首次实现纯雷达里程计，基于傅里叶SOFT在Boreas数据集上验证。
- 适合自动驾驶高速场景，如竞速超车中的实时感知。
本文倡导在频域处理雷达数据，以提升对噪声和结构误差的鲁棒性，尤其在高动态场景下——即搭载传感器的车辆自身高速运动，且存在未知数量的其他运动物体。相比基于特征的方法，频域处理不仅具备更高鲁棒性，还利用相关性方法可同时获取场景中所有运动结构的信息。以自动驾驶竞速中的超车动作为典型应用场景，本文展示了基于傅里叶SOFT的二维频域方法（FS2D）的初步实验结果，使用Boreas数据集验证了纯雷达里程计（radar-odometry）的可行性，无需传感器融合即可实现定位与跟踪。
原文摘要 · Abstract (English)
We promote in this paper the processing of radar data in the frequency domain to achieve higher robustness against noise and structural errors, especially in comparison to feature-based methods. This holds also for high dynamics in the scene, i.e., ego-motion of the vehicle with the sensor plus the presence of an unknown number of other moving objects. In addition to the high robustness, the processing in the frequency domain has the so far neglected advantage that the underlying correlation based methods used for, e.g., registration, provide information about all moving structures in the scene. A typical automotive application case is overtaking maneuvers, which in the context of autonomous racing are used here as a motivating example. Initial experiments and results with Fourier SOFT in 2D (FS2D) are presented that use the Boreas dataset to demonstrate radar-only-odometry, i.e., radar-odometry without sensor-fusion, to support our arguments.
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