A Machine Learning-Assisted Recognition and Compensation Method for UWB Ranging Errors in Complex Indoor Environments

Ultra-wideband (UWB) technology has been widely adopted for indoor positioning due to its high temporal resolution. However, the accuracy of UWB-based indoor positioning is fundamentally limited by ranging measurement errors, particularly under non-line-of-sight (NLOS) conditions, where systematic bias and uncertainty are introduced into the measured distances. In this paper, a measurement error mitigation method is proposed to improve UWB ranging reliability in complex indoor environments. The method first identifies NLOS measurements using low-dimensional physical features and a lightweight machine learning classifier. Subsequently, an error compensation strategy is applied to correct biased ranging observations, which are then incorporated into a nonlinear least squares positioning model. Experimental results obtained in typical indoor environments demonstrate that the proposed method significantly reduces ranging errors and improves positioning accuracy compared with conventional approaches. The results indicate that the proposed framework effectively enhances measurement robustness without increasing system complexity.