Among the various Ultra-wideband (UWB) ranging methods, the absence of uplink communication or centralized computation makes downlink time-difference-of-arrival (DL-TDOA) localization the most suitable for large-scale industrial deployments. However, temporary or permanent obstacles in the deployment region often lead to non-line-of-sight (NLOS) channel path and signal outage effects, which result in localization errors. Prior research has addressed this problem by increasing the ranging frequency, which leads to a heavy increase in the user device power consumption. It also does not contribute to any increase in localization accuracy under line-of-sight (LOS) conditions. In this paper, we propose and implement a novel low-power channel-aware dynamic frequency DL-TDOA ranging algorithm. It comprises NLOS probability predictor based on a convolutional neural network (CNN), a dynamic ranging frequency control module, and an IMU sensor-based ranging filter. Based on the conducted experiments, we show that the proposed algorithm achieves 50% higher accuracy in NLOS conditions while having 46% lower power consumption in LOS conditions compared to baseline methods from prior research.

翻译：在众多超宽带测距方法中，由于无需上行链路通信或集中式计算，下行到达时间差（DL-TDOA）定位成为大规模工业部署中最适用的方案。然而，部署区域中临时或永久性障碍物常导致非视距（NLOS）信道路径和信号中断效应，从而引发定位误差。先前研究通过提高测距频率来解决该问题，但这会导致用户设备功耗显著增加，且在视距（LOS）条件下无法提升定位精度。本文提出并实现了一种新型低功耗信道感知动态频率DL-TDOA测距算法，该算法包含基于卷积神经网络（CNN）的NLOS概率预测器、动态测距频率控制模块以及基于IMU传感器的测距滤波器。实验结果表明，与先前研究的基准方法相比，本文算法在NLOS条件下定位精度提升50%，同时在LOS条件下功耗降低46%。