<italic>DynaTrack</italic>: Low-Power Channel-Aware Dynamic Smartphone Tracking Using UWB DL-TDOA

Abstract

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, termed DynaTrack. It comprises a NLOS probability predictor based on a convolutional neural network (CNN), a dynamic ranging frequency control module, and an IMU sensor-based ranging filter. DynaTrack is implemented on Samsung Galaxy Note20 Ultra and Qorvo UWB board to show the feasibility and real-time applicability. Based on the conducted experiments, we show that DynaTrack achieves 41% higher accuracy in NLOS conditions while having 46% lower power consumption in LOS conditions compared to existing methods from prior research.

Keywords

• Ultra-wideband
• localization
• time difference of arrival
• recurrent neural network
• Kalman filter