  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.
