Reference coordinate based Chan algorithm for UWB personnel localization in underground coal mines

• Published in: Scientific reports Vol. 15; no. 1; pp. 17922 - 20
• Main Authors: Li, Zhipeng, Li, Zecheng
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.05.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/705; Accuracy; Adaptability; Adaptive algorithm; Algorithms; Coal; Coal mines; Coal mining; Computer applications; Downhole localization; Error compensation; Humanities and Social Sciences; Localization; Low-complexity localization; Mines; multidisciplinary; NLOS; Personnel; Science; Science (multidisciplinary); Ultra-wideband; Ultra-wideband; Downhole localization; Adaptive algorithm; NLOS; Error compensation; Low-complexity localization
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-03007-6

Ultra-wideband (UWB) positioning in coal mines faces severe accuracy degradation due to non-line-of-sight (NLOS) errors. To address this, we propose the Chan Based on Reference Coordinate (CBORF) algorithm, which integrates dynamic error compensation and adaptive parameter tuning to achieve centimeter-level accuracy with minimal computational overhead. Unlike existing methods (e.g., Chan-Taylor, Kalman-Chan), CBORF introduces a reference label-guided correction mechanism, statistically analyzing deviations between estimated and actual reference coordinates to compensate for systemic offset and dispersion errors. Simulations under exponential-distributed NLOS noise demonstrate CBORF’s superiority: RMSE of 0.026 m (stationary) and 0.075 m (moving targets), outperforming Chan (0.48 m) and Taylor (0.38 m) by 1–2 orders of magnitude. It also maintains the efficiency of the Chan algorithm and avoids iterative filtering (e.g., particle resampling in PF-Chan), which is a significant advantage over other algorithms. This work advances the state-of-the-art by resolving the long-standing trade-off between accuracy and computational complexity in NLOS-prone environments. Unlike filtering-dependent approaches (e.g., PF-Chan, Kalman-Chan), CBORF eliminates the need for iterative particle resampling or linear-Gaussian assumptions, ensuring reliability in high-noise, nonlinear conditions. Its parameter-driven design further enhances adaptability across diverse underground layouts, offering a practical and scalable solution for real-time personnel tracking in coal mines.