An improved UWB/PDR integrated system for pedestrian wearable indoor localization

Abstract With the rapid progress of internet of things applications, wearable indoor localization techniques combining ultra-wideband (UWB) with inertial measurement units (IMUs) have gained increasing attention. However, the non-line-of-sight (NLoS) obstruction and multipath (MP) effects of UWB, along with the inherent cumulative error of IMUs, remain critical challenges that limit the localization accuracy and constrain their practical applications. This investigation proposes an improved UWB/IMU integrated system with adaptive tight coupling data fusion for pedestrian wearable localization. It enhances UWB ranging accuracy and pedestrian dead reckoning (PDR) step length estimation precision by leveraging UWB NLoS and MP identification and dynamic error compensation, alongside an adaptive PDR calibration mechanism to improve the accuracy of step length estimation. Firstly, a lightweight support vector machine (SVM)-based NLoS/MP detection method is proposed, which employs the maximum relevance minimum redundancy algorithm to select the optimal feature subset, and uses radial basis function-SVM for classification. Secondly, an adaptive PDR calibration mechanism based on UWB displacement reference is established to achieve step length estimation, which enhances the system’s dynamic adaptability to user gait variations in different scenarios. Then, an adaptive federated extended Kalman filter (AFEKF) framework is constructed, which achieves a tight combination fusion of the corrected UWB and calibrated PDR by dynamically weighting the UWB NLoS/MP state using posterior probability. Experimental results demonstrate that the proposed AFEKF tightly-coupled localization algorithm achieves a significantly lower root mean square error in complex indoor environments compared to conventional extended Kalman filter (EKF) and federated EKF tightly-coupled algorithms. By effectively suppressing the impact of raw anomalous measurements on the filter and adapting to user gait variations in real-time, the algorithm significantly enhances the localization accuracy, robustness, and environmental adaptability.