A Robust GNSS/PDR Integration Scheme with GRU-Based Zero-Velocity Detection for Mass-Pedestrians

Dongpeng Xie,Peihui Yan,Jingnan Liu,Yanan Tang,Jiaji Wu,Jinguang Jiang,Chao Zhang

doi:10.3390/rs14020300

Abstract

Aiming at the problem of high-precision positioning of mass-pedestrians with low-cost sensors, a robust single-antenna Global Navigation Satellite System (GNSS)/Pedestrian Dead Reckoning (PDR) integration scheme is proposed with Gate Recurrent Unit (GRU)-based zero-velocity detector. Based on the foot-mounted pedestrian navigation system, the error state extended Kalman filter (EKF) framework is used to fuse GNSS position, zero-velocity state, barometer elevation, and other information. The main algorithms include improved carrier phase smoothing pseudo-range GNSS single-point positioning, GRU-based zero-velocity detection, and adaptive fusion algorithm of GNSS and PDR. Finally, the scheme was tested. The root mean square error (RMSE) of the horizontal error in the open and complex environments is lower than 1 m and 1.5 m respectively. In the indoor elevation experiment where the elevation difference of upstairs and downstairs exceeds 25 m, the elevation error is lower than 1 m. This result can provide technical reference for the accurate and continuous acquisition of public pedestrian location information.

Highlights

The joint application of low-cost inertial sensors, miniature global navigation satellite system (GNSS) receivers, and barometers has been one of the research hotspots in the field of navigation in the past decade [1,2,3]
When carrying out the walking experiment, the inertial measurement units (IMU) is fixed on the surface of the shoe, and the GNSS receiver antenna is set on the top of the head
Thewhich performance parameters of the gyroscope and accelerometer are shown in Table of theisgyroscope and accelerometer are shown in GNSS module the mosaic-X5 produced by Septentrio, Belgium, The experimental modulesupporting is the mosaic-X5 produced by Septentrio, which is used to track all visible satellites at the same time

Summary

Introduction

The joint application of low-cost inertial sensors, miniature global navigation satellite system (GNSS) receivers, and barometers has been one of the research hotspots in the field of navigation in the past decade [1,2,3]. Cameras [11] and radar [12] can improve the robustness of the system, these sensors only work effectively when there are enough features in the environment, which limits their application Some solutions such as multi-source information fusion have been proposed around the demand for continuous and reliable pedestrian navigation. GNSS single-point positioning algorithm, GRU-based zero-velocity gait detection, and of the scheme are introduced, including improved carrier phase smoothing pseudo-range adaptive fusion algorithm of GNSS and PDR. The fourth section introduces the experiGNSS single-point positioning algorithm, GRU-based zero-velocity gait detection, and ment results and analysis. The algorithm positioning results for low-cost mass pedestrian positioning complex framework, considering the gait characteristics of pedestrian walking, and fusingframeGNSS This integration scheme uses the error state EKF as the algorithm position, barometer height, and other information. M where ρ(t) and φ(t) are the distances of the observed pseudo-range and carrier phase, respectively, and M is the length of the filter, which is determined according to the smoothing time T and the observation sampling τ. δφ(tk , tk−1 ) is the difference operator

GRU-Based Zero-Velocity Detection

Lever Correction

Results

The Proposed Algorithm Performance Verification under Complex Environment

Indoor Elevation Performance Verification of the Proposed Algorithm

Conclusions