标题： 基于商用UWB收发器的无设备定位 显示英文标题

标题： Device-Free Localization Using Commercial UWB Transceivers

摘要： 近年来，商用超宽带（UWB）收发器不仅能够测量设备间的距离，还能跟踪不携带UWB设备的行人位置。基于UWB的无设备定位不需要专用雷达设备，与现有锚点基础设施兼容，可以重复使用以降低硬件部署成本。然而，由于信号噪声比（SNR）低和杂乱的环境，难以在现实场景中准确估计目标位置。在本文中，我们提出了一种深度学习（DL）辅助的粒子滤波器来克服这些挑战。首先，分析信道脉冲响应（CIR）方差以捕捉目标移动引起的变异。然后，使用基于DL的一维注意力U-Net来仅提取由目标引起的反射成分，并抑制CIR方差图中的噪声成分。最后，将多个预处理的CIR方差图作为输入，通过粒子滤波器估计目标的位置。实验结果表明，所提出的系统是物联网和汽车应用中一种实用且经济的解决方案，均方根误差（RMSE）约为15厘米，平均处理时间为4毫秒。此外，与现有最先进方法的比较显示，所提出的方法在合理的计算成本下提供了最佳性能。 显示英文摘要

摘要： Recently, commercial ultra-wideband (UWB) transceivers have enabled not only measuring device-to-device distance but also tracking the position of a pedestrian who does not carry a UWB device. UWB-based device-free localization that does not require dedicated radar equipment is compatible with existing anchor infrastructure and can be reused to reduce hardware deployment costs. However, it is difficult to estimate the target's position accurately in real-world scenarios due to the low signal-to-noise ratio (SNR) and the cluttered environment. In this paper, we propose a deep learning (DL)-assisted particle filter to overcome these challenges. First, the channel impulse response (CIR) variance is analyzed to capture the variability induced by the target's movement. Then, a DL-based one-dimensional attention U-Net is used to extract only the reflection components caused by the target and suppress the noise components within the CIR variance profile. Finally, multiple preprocessed CIR variance profiles are used as input to a particle filter to estimate the target's position. Experimental results demonstrate that the proposed system is a practical and cost-effective solution for IoT and automotive applications with a root mean square error (RMSE) of about 15 cm and an average processing time of 4 ms. Furthermore, comparisons with existing state-of-the-art methods show that the proposed method provides the best performance with reasonable computational costs.