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.
Highlights
Indoor positioning technology has a wide range of applications from supermarket shopping assistance to drone positioning and patient tracking in hospitals [1,2,3]
In many practical scenarios, such as warehouse robot positioning and emergency response in crowded scenes, UWB signals may be blocked by people, cargos, or other obstacles, which will cause signal problems such as multipath effect, strength attenuation, and even signal loss, resulting in a sharp drop in the UWB positioning accuracy [4, 5]. e fusion of inertial measurement unit (IMU) in a pedestrian dead reckoning (PDR) method and UWB is an effective way to achieve highprecision positioning in nonline-of-sight (NLOS) environments
At least three effective UWB range measurements are required for this method, which may not be available under the NLOS conditions
Summary
Indoor positioning technology has a wide range of applications from supermarket shopping assistance to drone positioning and patient tracking in hospitals [1,2,3]. E fusion of inertial measurement unit (IMU) in a pedestrian dead reckoning (PDR) method and UWB is an effective way to achieve highprecision positioning in nonline-of-sight (NLOS) environments. Gonzalez et al [13] used particle filter algorithm to fuse the data of UWB, IMU, and odometer, achieving good positioning stability under the NLOS conditions. To address the above challenges, in this paper, we propose a tightly coupled UWB/PDR fusion method based on smartphone IMU for positioning in indoor environments. E remainder of the paper is organized as follows: In Section 2, the PDR algorithm based on smartphone inertial sensor is discussed and the theoretical analysis and experimental results of step detection, step length calculation, and heading calculation are given.
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