Robust IMU/UWB integration for indoor pedestrian navigation

Abstract

Usually in Pedestrian navigation, indirect Kalman filtering approach is used for sensors fusion. In this research, it is proposed to outperform this approach by the use of direct filtering method. Based on MEMS IMU and UWB positioning, we propose the use of modern algorithms developed recently with modified version of EKF; Sigma Point Kalman Filters (SPKF), and recently developed Cubature Kalman Filter (CKF) as a superior alternative to standard filters. The CKF improves the mean and covariance propagation consequently comparing with EKF and SPKF (UKF, CDKF). Although the CKF provides a better estimate of the orientation, velocity and position with Zero velocity UPdaTes (ZUPT) and Zero Angular Rate UpdaTes (ZARUT) measurements. Robust IMU/UWB navigation system is achieved based on robust Student-t based extended Kalman filter and other variants.