Power-Efficient Indoor Localization Using Adaptive Channel-aware Ultra-wideband DL-TDOA
This addresses power efficiency and accuracy issues for large-scale industrial deployments using UWB localization, representing an incremental improvement over prior methods.
The paper tackles the problem of localization errors in Ultra-wideband DL-TDOA systems caused by non-line-of-sight conditions and signal outages, proposing a channel-aware dynamic frequency algorithm that achieves 50% higher accuracy in NLOS conditions and 46% lower power consumption in LOS conditions compared to baselines.
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.