Accurate, tag-free distance estimation with ultrawideband (UWB) radar is essential for applications such as autonomous guided vehicles, robotics, and environment characterization. For tag-based localization systems, phase-based UWB signal processing techniques have demonstrated sub-wavelength ranging precision, but these approaches are not applicable for passive (tagless) radar setups with weak reflections, mixed multipath conditions, and the absence of a known time-of-flight (ToF) first-path reference. This paper demonstrates for the first time that phase information can be effectively exploited in a fully passive UWB radar setting. We introduce a signal processing framework that extracts reliable distance information by combining coarse amplitude-based estimates with high-resolution phase changes across multiple frequency channels. By referencing phase measurements with the line-of-sight component, the method compensates for hardware-induced phase drift, while the use of multichannel frequency diversity enables disambiguation of periodic phase information and improves robustness against frequencyspecific channel degradation such as Fresnel zones. The proposed approach is validated on a robot equipped with a bistatic UWB radar using DW3000 devices and evaluated in a realistic metallic industrial environment. Experimental results show that our work consistently achieves centimeter-level accuracy even at high speeds, with a median error of 1.69 cm, significantly outperforming existing ~10cm accuracy UWB radar approaches relying only on amplitude-information. We further show how multi-channel fusion exploits uncorrelated channel degradation to reduce the error by more than 40% compared to single-channel operation, and outline how phase modeling and fusion can be pushed toward sub-centimeter accuracy.

翻译：超宽带（UWB）雷达的无标签精确距离估计对于自动导引车、机器人及环境表征等应用至关重要。对于基于标签的定位系统，基于相位的UWB信号处理技术已实现亚波长级测距精度，但这些方法不适用于无源（无标签）雷达场景——该场景面临弱反射信号、混合多径条件以及缺乏已知飞行时间（ToF）首径参考的挑战。本文首次证明，在完全无源UWB雷达设置中可有效利用相位信息。我们提出一种信号处理框架，通过融合粗粒度幅度估计与多频通道间的高分辨率相位变化来提取可靠距离信息。该方法以视距分量为相位测量基准，补偿硬件引起的相位漂移；同时利用多通道频率分集解决周期性相位模糊问题，并提升对菲涅尔区等频率特定信道劣化的鲁棒性。所提方法在搭载采用DW3000设备构建的双站UWB雷达的机器人平台上验证，并在真实金属工业环境中评估。实验结果表明，本工作即使在高动态场景下也能稳定实现厘米级精度，中位误差为1.69厘米，显著优于仅依赖幅度信息的现有~10厘米精度UWB雷达方法。我们进一步证明，多通道融合利用非相关信道劣化可相比单通道操作降低误差超过40%，并阐释如何通过相位建模与融合将精度推向亚厘米级。