An Advanced Indoor Localization Method Based on xLSTM and Residual Multimodal Fusion of UWB/IMU Data

To address the limitations of single-modality UWB/IMU systems in complex indoor environments, this study proposes a multimodal fusion localization method based on xLSTM. After extracting features from UWB and IMU data, the xLSTM network enables deep temporal feature learning. A three-stage residual fusion module is introduced to enhance cross-modal complementarity, while a multi-head attention mechanism dynamically adjusts the sensor weights. The end-to-end trained network effectively constructs nonlinear multimodal mappings for two-dimensional position estimation under both static and dynamic non-line-of-sight (NLOS) conditions with human-induced interference. Experimental results demonstrate that the localization errors reach 0.181 m under static NLOS and 0.187 m under dynamic NLOS, substantially outperforming traditional filtering-based approaches. The proposed deep fusion framework significantly improves localization reliability under occlusion and offers an innovative solution for high-precision indoor positioning.