Dynamic Spatio–Temporal Imputation for Robust UWB Ranging in Urban Subway Localization

Ultra-Wideband (UWB) technology enables high-precision positioning in underground subway environments with poor GNSS signal coverage, but ranging measurements often suffer from data loss due to occlusions, multipath effects, and ranging failures. While recent studies have focused on imputing missing measurements using temporal modeling, they often overlook spatial factors such as anchor group vary and train motion patterns. To address this limitation, this paper proposes a novel approach for imputing missing UWB ranging data by jointly exploiting temporal dynamics and spatial constraints. A mask matrix is introduced to model data availability, and a spatio-temporal optimization framework is established to guide the imputation process. Based on this framework, a Dynamic Spatio-Temporal Missing Data Imputation Network (DSTMIN) is developed for robust localization in complex subway environments. In particular, DSTMIN leverages missing-aware attention and adaptive graph fusion, significantly enhancing its robustness to large missing blocks and non-random data loss. Simulation results demonstrate that DSTMIN outperforms state-of-the-art models, such as STGCN and GRIN, under various missing rates. Under a 40% ranging data missing ratio, DSTMIN reduces the root mean square error (RMSE) by 18.6% and 16.3% compared with STGCN and GRIN, respectively.