A Tightly Coupled Fusion Method of UWB and IMU for Indoor Positioning

Ultra-wideband (UWB) technology has been widely applied to indoor positioning due to its high-precision ranging. In practical indoor scenarios, inertial measurement units (IMUs) are commonly fused with UWB to suppress measurement errors caused by non-line-of-sight (NLOS) propagation. Frequent switching between line-of-sight (LOS) and NLOS conditions easily generates severe outliers in UWB ranging data. Furthermore, the conventional extended Kalman filter (EKF) lacks an adaptive adjustment mechanism for innovation covariance, which significantly reduces the positioning robustness of the fusion system. To address the above issues, this paper establishes a tightly coupled UWB/IMU positioning model for complex indoor scenarios and proposes an improved robust adaptive extended Kalman filter (IR-AEKF) algorithm. After calibrating UWB ranging errors, IMU bias, and scale factor errors, the system state equation and UWB ranging observation model are constructed under the tightly coupled fusion framework. On this basis, innovation statistics are leveraged to incorporate Huber-type weights and adaptive scaling factors that dynamically tune the observation noise covariance matrix. Meanwhile, a ranging outlier elimination threshold is set to suppress gross errors induced by NLOS interference. Experimental results demonstrate that the IR-AEKF can effectively suppress disturbances arising from abnormal ranging outliers. In dynamic positioning tests, the mean positioning errors along the X-axis and Y-axis are 0.032 m and 0.035 m, respectively. The IR-AEKF provides an effective technical solution for UWB/IMU fusion positioning in complex indoor environments.