Dynamic Signal Enhancement and Dual Robust Filtering for NLOS-Resilient Indoor Localization

In this paper, we propose an INS/UWB fusion localization method that integrates adaptive Student's t Kalman filtering with a progressive particle filtering strategy to address the challenges posed by non-line-of-sight (NLOS) errors. The UWB data are preprocessed using dichotomous k-medoid clustering, while the IMU data are denoised through variational mode decomposition and adaptive thresholding. The processed data are then effectively fused through a dual-filtering approach. Experimental results demonstrate that the proposed algorithm significantly enhances localization accuracy in NLOS environments, offering superior stability and performance compared to traditional methods.