Adaptive Kalman Filter-Based UWB Location Tracking with Optimized DS-TWR in Workshop Non-Line-of-Sight Environments

Abstract

At the current stage, indoor Ultra-Wideband (UWB) positioning systems often encounter challenges in achieving high localization accuracy under non-line-of-sight (NLOS) conditions within workshop environments when employing the Double-Sided Two-Way Ranging (DS-TWR) algorithm. To address this issue, a positioning optimization method based on the DS-TWR algorithm is proposed. By streamlining message exchanges between nodes, the method reduces node energy consumption and shortens ranging time, thereby enhancing system energy efficiency and response speed. Furthermore, to improve positioning accuracy in workshop NLOS environments, an Adaptive Kalman Filtering algorithm is introduced. This algorithm dynamically evaluates the influence of obstruction information caused by NLOS conditions on the covariance of observation noise and adaptively adjusts the filtering gain of the signals accordingly. Through this approach, the system can effectively eliminate invalid positioning information in signals, mitigate the adverse effects of NLOS conditions on positioning accuracy and achieve more precise localization. Experimental results demonstrate that the proposed optimization algorithm achieves substantial performance improvements in both static and dynamic positioning experiments under workshop NLOS conditions. Specifically, the algorithm not only enhances system positioning accuracy but also further strengthens the real-time ranging precision of the DS-TWR algorithm.