Tightly Coupling Fusion of UWB Ranging and IMU Pedestrian Dead Reckoning for Indoor Localization

Ultra-wideband (UWB) and inertial measurement unit (IMU) fusion is an efficient method to resolve the uncertainties of UWB in non-line-of-sight (NLOS) situations because of signals refraction, the effect of multipath and inertial positioning error accumulation in indoor environments. Existing system...

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Main Authors: Rashid Ali, Ran Liu, Anand Nayyar, Basit Qureshi, Zhiqiang Cao
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
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Online Access:https://ieeexplore.ieee.org/document/9635776/
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author Rashid Ali
Ran Liu
Anand Nayyar
Basit Qureshi
Zhiqiang Cao
author_facet Rashid Ali
Ran Liu
Anand Nayyar
Basit Qureshi
Zhiqiang Cao
author_sort Rashid Ali
collection DOAJ
description Ultra-wideband (UWB) and inertial measurement unit (IMU) fusion is an efficient method to resolve the uncertainties of UWB in non-line-of-sight (NLOS) situations because of signals refraction, the effect of multipath and inertial positioning error accumulation in indoor environments. Existing systems, however, are focused only on foot-mounted IMUs that restrict the system’s implementation to particular real situations. In this research, using foot-mounted IMU, we suggest combining UWB ranging and IMU pedestrian dead reckoning (PDR), which can provide a generic indoor positioning solution. The issues such as position and orientation drift, interferences and divergence in strap-down inertial navigation system (SINS) based orientation estimates could be addressed by a UWB ranging sensor fusing with an IMU using the extended Kalman filter (EKF). The main goal of this research is to investigate and compare two different sensor data fusion techniques. For instance, adaptive Kalman filter (AKF) and least-squares (LSs) incorporate a foot-mounted IMU tightly coupled to a 2D pedestrian positioning solution derived from UWB signals. Moreover, we consider the UWB NLOS and IMU error identification. A real-time ranging error compensation model based on the LS method and AKF positioning algorithm are used for fixing such problems. We propose a new tightly coupled inertial navigation system (INS) with a two-way ranging (TWR) fusion positioning algorithm to improve accuracy, integrating UWB and IMU sensors based on the EKF in pedestrian navigation. Experiments in dynamic indoor environment validate the effectiveness of the proposed approach that uses EKF to combine AKF and LS for error minimization.
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spelling doaj-art-43bbd043228445cb9ad859b845a30d3f2025-08-20T02:16:40ZengIEEEIEEE Access2169-35362021-01-01916420616422210.1109/ACCESS.2021.31326459635776Tightly Coupling Fusion of UWB Ranging and IMU Pedestrian Dead Reckoning for Indoor LocalizationRashid Ali0https://orcid.org/0000-0003-0889-8890Ran Liu1Anand Nayyar2https://orcid.org/0000-0002-9821-6146Basit Qureshi3https://orcid.org/0000-0001-7389-519XZhiqiang Cao4School of Information Engineering, Southwest University of Science and Technology, Mianyang, ChinaSchool of Information Engineering, Southwest University of Science and Technology, Mianyang, ChinaGraduate School, Duy Tan University, Da Nang, VietnamDepartment of Computer Science, Prince Sultan University, Riyadh, Saudi ArabiaSchool of Information Engineering, Southwest University of Science and Technology, Mianyang, ChinaUltra-wideband (UWB) and inertial measurement unit (IMU) fusion is an efficient method to resolve the uncertainties of UWB in non-line-of-sight (NLOS) situations because of signals refraction, the effect of multipath and inertial positioning error accumulation in indoor environments. Existing systems, however, are focused only on foot-mounted IMUs that restrict the system’s implementation to particular real situations. In this research, using foot-mounted IMU, we suggest combining UWB ranging and IMU pedestrian dead reckoning (PDR), which can provide a generic indoor positioning solution. The issues such as position and orientation drift, interferences and divergence in strap-down inertial navigation system (SINS) based orientation estimates could be addressed by a UWB ranging sensor fusing with an IMU using the extended Kalman filter (EKF). The main goal of this research is to investigate and compare two different sensor data fusion techniques. For instance, adaptive Kalman filter (AKF) and least-squares (LSs) incorporate a foot-mounted IMU tightly coupled to a 2D pedestrian positioning solution derived from UWB signals. Moreover, we consider the UWB NLOS and IMU error identification. A real-time ranging error compensation model based on the LS method and AKF positioning algorithm are used for fixing such problems. We propose a new tightly coupled inertial navigation system (INS) with a two-way ranging (TWR) fusion positioning algorithm to improve accuracy, integrating UWB and IMU sensors based on the EKF in pedestrian navigation. Experiments in dynamic indoor environment validate the effectiveness of the proposed approach that uses EKF to combine AKF and LS for error minimization.https://ieeexplore.ieee.org/document/9635776/Foot-mounted IMUpedestrianindoor navigationEKFtightly couplingUWB
spellingShingle Rashid Ali
Ran Liu
Anand Nayyar
Basit Qureshi
Zhiqiang Cao
Tightly Coupling Fusion of UWB Ranging and IMU Pedestrian Dead Reckoning for Indoor Localization
IEEE Access
Foot-mounted IMU
pedestrian
indoor navigation
EKF
tightly coupling
UWB
title Tightly Coupling Fusion of UWB Ranging and IMU Pedestrian Dead Reckoning for Indoor Localization
title_full Tightly Coupling Fusion of UWB Ranging and IMU Pedestrian Dead Reckoning for Indoor Localization
title_fullStr Tightly Coupling Fusion of UWB Ranging and IMU Pedestrian Dead Reckoning for Indoor Localization
title_full_unstemmed Tightly Coupling Fusion of UWB Ranging and IMU Pedestrian Dead Reckoning for Indoor Localization
title_short Tightly Coupling Fusion of UWB Ranging and IMU Pedestrian Dead Reckoning for Indoor Localization
title_sort tightly coupling fusion of uwb ranging and imu pedestrian dead reckoning for indoor localization
topic Foot-mounted IMU
pedestrian
indoor navigation
EKF
tightly coupling
UWB
url https://ieeexplore.ieee.org/document/9635776/
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AT ranliu tightlycouplingfusionofuwbrangingandimupedestriandeadreckoningforindoorlocalization
AT anandnayyar tightlycouplingfusionofuwbrangingandimupedestriandeadreckoningforindoorlocalization
AT basitqureshi tightlycouplingfusionofuwbrangingandimupedestriandeadreckoningforindoorlocalization
AT zhiqiangcao tightlycouplingfusionofuwbrangingandimupedestriandeadreckoningforindoorlocalization