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

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.