Parallel Deep Learning for NLOS Detection and Error Mitigation in UWB Positioning

Ultra-wideband (UWB) technology is extensively applied in indoor high-precision localization scenarios. However, barriers along the radio signal path can lead to Non-Line-of-Sight (NLOS) propagation, thereby reducing positioning reliability. Therefore, identifying NLOS conditions and mitigating the associated errors is essential. In this paper, a novel NLOS detection and mitigation approach is introduced, leveraging the parallel Spatio-Temporal feature fusion network (PSTFFN) and Spatio-Temporal attention regression network (STARN). We utilize continuous wavelet transform to convert the one-dimensional channel impulse response (CIR) signal into a two-dimensional time-frequency diagram of CIR image data. By incorporating an attention mechanism and handcrafted features, PSTFFN enhances its ability to differentiate between scenarios with similar features. Based on the NLOS classification results, the proposed STARN model, tailored to specific scenarios, further mitigates NLOS errors. Experimental results indicate that the proposed method performs highly in both binary and multi-class NLOS classification, with accuracy rates exceeding 96.74%. Moreover, using this approach, the root mean square error (RMSE) and mean absolute error (MAE) of distance measurements are reduced from 168.04 cm and 106.86 cm to 7.54 cm and 7.78 cm, respectively. In real-world indoor positioning experiments, this method improves positioning accuracy by over 81.86% and achieves real-time response performance.