UWBPDR Tightly Coupled Navigation with Robust Extended Kalman Filter for NLOS Environments
Joint Authors
Li, Xin
Wang, Yan
Khoshelham, Kourosh
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-05
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Telecommunications Engineering
Abstract EN
The fusion of ultra-wideband (UWB) and inertial measurement unit (IMU) is an effective solution to overcome the challenges of UWB in nonline-of-sight (NLOS) conditions and error accumulation of inertial positioning in indoor environments.
However, existing systems are based on foot-mounted or body-worn IMUs, which limit the application of the system to specific practical scenarios.
In this paper, we propose the fusion of UWB and pedestrian dead reckoning (PDR) using smartphone IMU, which has the potential to provide a universal solution to indoor positioning.
The PDR algorithm is based on low-pass filtering of acceleration data and time thresholding to estimate the step length.
According to different movement patterns of pedestrians, such as walking and running, several step models are comparatively analyzed to determine the appropriate model and related parameters of the step length.
For the PDR direction calculation, the Madgwick algorithm is adopted to improve the calculation accuracy of the heading algorithm.
The proposed UWB/PDR fusion algorithm is based on the extended Kalman filter (EKF), in which the Mahalanobis distance from the observation to the prior distribution is used to suppress the influence of abnormal UWB data on the positioning results.
Experimental results show that the algorithm is robust to the intermittent noise, continuous noise, signal interruption, and other abnormalities of the UWB data.
American Psychological Association (APA)
Li, Xin& Wang, Yan& Khoshelham, Kourosh. 2018. UWBPDR Tightly Coupled Navigation with Robust Extended Kalman Filter for NLOS Environments. Mobile Information Systems،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1204983
Modern Language Association (MLA)
Li, Xin…[et al.]. UWBPDR Tightly Coupled Navigation with Robust Extended Kalman Filter for NLOS Environments. Mobile Information Systems No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1204983
American Medical Association (AMA)
Li, Xin& Wang, Yan& Khoshelham, Kourosh. UWBPDR Tightly Coupled Navigation with Robust Extended Kalman Filter for NLOS Environments. Mobile Information Systems. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1204983
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1204983