UWB/IMU Integrated Navigation Method Based on Enhanced IMM Processing

Ultra-Wide Band (UWB) signals in indoor environments might suffer from positive deviation errors due to the influence of Non-Line-of-Sight (NLOS), which leads to inaccurate positioning results. To address the problem, we propose an Error State Kalman Filter (ESKF) fusion algorithm based on enhanced interactive-multi-model (EAIMM), named as EAIMM-ESKF. Firstly, we establish distance models for UWB tags and base stations in LOS and NLOS environments. Moreover, we introduce the dynamically updated probability matrix and forgetting factor to reduce the misjudgment rate of the IMM model and adaptively determine the NLOS deviation. Finally, a fusion framework for UWB and IMU based on ESKF is established. Experimental data indicate that under NLOS occlusion conditions, our proposed approach has a more significant suppression effect on NLOS ranging errors compared to other algorithms, which can effectively improve positioning accuracy and perform better under some complex occlusion conditions.