Investigation of UWB-IMU sensor fusion for indoor navigation with DoE

Abstract

This study presents an evaluation of the optimal parameter configuration for Ultra-Wide Band (UWB) - Inertial Measurement Units - (IMU) based sensor fusion for indoor localization in Non-Line-of-Sight (NLOS) environments. The study employs the least squares method to predict position using UWB technology. Subsequently, sensor fusion techniques combining UWB and IMU are employed, utilizing the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) algorithms to enhance position estimation. To mitigate the effects of noise in IMU data, a high-pass filter is applied before feeding the data into the EKF and UKF. The experimental findings are then evaluated using Design of Experiment (DoE) techniques, and the optimal parameter configurations are analysed using linear regression. This study provides insight into the parameter settings that yield improved accuracy and robustness in UWB-IMU sensor fusion for indoor localization in NLOS scenarios.