Online cubature Kalman filter Rauch–Tung–Striebel smoothing for indoor inertial navigation system/ultrawideband integrated pedestrian navigation

Abstract

Accurate position information of the pedestrians is required in many applications such as healthcare, entertainment industries, and military field. In this work, an online Cubature Kalman filter Rauch–Tung–Striebel smoothing algorithm for people’s location in indoor environment is proposed using inertial navigation system techniques with ultrawideband technology. In this algorithm, Cubature Kalman filter is employed to improve the filtering output accuracy; then, the Rauch–Tung–Striebel smoothing is used between the ultrawideband measurements updates; finally, the average value of the corrected inertial navigation system error estimation is output to compensate the inertial navigation system position error. Moreover, a real indoor test has been done for assessing the performance of the proposed model and algorithm. Test results show that the proposed model is able to reduce the sum of the absolute position error between the east direction and the north direction by about 32% compared with only the ultrawideband model, and the performance of the online Cubature Kalman filter Rauch–Tung–Striebel smoothing algorithm is slightly better than the off-line mode.