Citation: Xie, D.; Jiang, J.; Wu, J.; Yan,
P.; Tang, Y.; Zhang, C.; Liu, J. A
Robust GNSS/PDR Integration
Scheme with GRU-Based Zero-
Velocity Detection for Mass-Pedestrians.
Remote Sens. 2022, 14, 300. https://
doi.org/10.3390/rs14020300
Academic Editor: Kamil Krasuski
Received: 5 December 2021
Accepted: 4 January 2022
Published: 10 January 2022
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Article
A Robust GNSS/PDR Integration Scheme with GRU-Based
Zero-Velocity Detection for Mass-Pedestrians
Dongpeng Xie
1
, Jinguang Jiang
2,
* , Jiaji Wu
2
, Peihui Yan
2
, Yanan Tang
2
, Chao Zhang
2
and Jingnan Liu
2
1
Electronic Information School, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China;
dongpengxie@whu.edu.cn
2
GNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China;
aang_wu@whu.edu.cn (J.W.); phuiyan@whu.edu.cn (P.Y.); lucytang@whu.edu.cn (Y.T.);
chaozz@whu.edu.cn (C.Z.); jnliu@whu.edu.cn (J.L.)
* Correspondence: jinguang@whu.edu.cn
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 Reck-
oning (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.
Keywords: pedestrian navigation; GRU-based zero-velocity detection; integration navigation
1. 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
–
3
]. Accurate and continuous pedestrian location
information is widely used in professional applications such as armed police duty, field
walking, pipeline maintenance, etc. The satellite navigation system can provide users with
good location services in an open outdoor environment [
4
,
5
]. However, the system cannot
work well in some scenarios where GNSS signals are unavailable [
6
,
7
]. Other information
sources such as wireless local area networks (WLAN) [
8
], ultra-wideband (UWB) [
9
], radio
frequency identification (RFID) [
10
], etc., can directly provide location information. How-
ever, there is a problem of signal occlusion in indoor scenarios. Generally, WiFi positioning
and UWB positioning require extensive installation of signal receiving devices during the
actual operation. Whether fingerprint positioning or trilateral measurement, WiFi posi-
tioning cannot be separated from offline surveying and mapping, limiting its application
mode. Cameras [
11
] and radar [
12
] can improve the robustness of the system, however,
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. Zhu et al. proposed a novel
pedestrian navigation system (PNS) integrating RTK/Pseudolite/LAHDE/IMU, which
uses manmade landmarks deployed at the entrance of the corridor to determine whether
pedestrians are in an indoor corridor. If pedestrians do not follow a route with landmarks,
Remote Sens. 2022, 14, 300. https://doi.org/10.3390/rs14020300 https://www.mdpi.com/journal/remotesensing
