The Improved Constraint Methods for Foot-Mounted Pedestrian Three-Dimensional Inertial Navigation

Xiaomeng Wu,Lintong Zhang,Shuli Guo,Liying Zhao

doi:10.1155/2021/2048058

Abstract

The foot-mounted pedestrian navigation system (PNS) that uses microelectromechanical systems (MEMS) inertial measurement units (IMUs) to track the person’s position. However errors accumulate over time during inertial navigation solutions, which affects the positioning precision. In this paper, a multicondition zero velocity detector is used to detect the stance phase of gait. Then the errors are corrected in the stance phase and the swing phase, respectively, through the Kalman filter. When pedestrians are going up and down the stairs, the divergence of height will reduce the accuracy of three-dimensional positioning. In this paper, an accelerometer and a barometer are used to obtain altitude variation, and after that the stair condition detection (SCD) algorithm is proposed to correct the height of Kalman filter output and detect the walking state of pedestrians. Through theoretical research and field experiments, these algorithms are studied carefully. The results of the experiment show that the algorithm proposed in this paper can effectively eliminate errors and achieve more accurate positioning.

Highlights

With the development of smart city, people’s demand for location service is getting higher and higher
According to the literature [1], current indoor positioning solutions can be divided into two categories: One is wireless communication positioning which relies on preinstalled installations like wireless (Wi-Fi) [2], Bluetooth [3], infrared [4, 5], Ultra-Wide Band (UWB) [6, 7], etc. e second method is based on the MEMS inertial measurement unit (IMU) to form an inertial navigation system (INS) for pedestrians to track trajectories [8, 9]
In order to further improve the accuracy of positioning, it is not enough to only use the error correction in the stance phase. erefore, this paper corrects the error of the stance phase, and corrects the error of the swing phase [28]

Summary

Introduction

With the development of smart city, people’s demand for location service is getting higher and higher. E most important step to use ZUPT algorithm is to detect the stance phase of the gait. E literature [11] uses the accelerometer or the gyroscope output to detect the stance phase. E literature [18] uses a magnetometer sensor as a compass to estimate the heading drift error. The zero angular rate update (ZARU) and the heuristic heading drift estimation method based on the main direction angle (MHDE) are proposed to correct the heading error. ZARU method mainly uses the difference between angular rate output and zero angular rate in the stance phase as observations of the Kalman filter to correct the heading error.

Error Model

Experiment

50 Real heading

Findings

Conclusion