Paper Info
Title
UWB/PDR Tightly Coupled Navigation with Robust Extended Kalman Filter for NLOS Environments.
Abstract
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.
Year
DOI
Venue
2018
10.1155/2018/8019581
MOBILE INFORMATION SYSTEMS
Field
DocType
Volume
Non-line-of-sight propagation,Computer vision,Extended Kalman filter,Computer science,Filter (signal processing),Mahalanobis distance,Dead reckoning,Acceleration,Inertial measurement unit,Artificial intelligence,Thresholding,Distributed computing
Journal
2018
ISSN
Citations 
PageRank 
1574-017X
0
0.34
References 
Authors
8
3
Name
Order
Citations
PageRank
Xin Li149568.25
Yan Wang220.70
Kourosh Khoshelham36512.67