A Real-Time GNSS/PDR Navigation System for Mobile Devices

Michele Basso,Fabrizio Sera,Simone Morosi,Alessio Martinelli

doi:10.3390/rs13081567

Michele Basso, Fabrizio Sera + Show 2 more

Open Access

https://doi.org/10.3390/rs13081567

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Journal: Remote sensing	Publication Date: Apr 18, 2021
Citations: 12	License type: CC BY 4.0

Affiliation: University of Florence

Abstract

In this article, a smart pedestrian navigation system is developed to be implemented in a common smartphone. The main phases that characterize a pedestrian navigation system that is based on dead reckoning are introduced. A suitable Phase-Locked Loop is designed and the algorithm to estimate the direction of the user’s motion between one step and the next is developed. Finally, a suitable multi-rate Kalman filter (KF) is considered to merge the information from the pedestrian dead reckoning (PDR) navigation with the data provided by the global navigation satellite systems (GNSS). The proposed GNSS/PDR navigation system is implemented in Simulink as a finite-state machine and allows to define a trade-off between energy-saving and performance improvement in terms of position accuracy. The presented pedestrian navigation system is independent of the body-worn location of the smartphone and implements a compensation strategy of the systematic errors that are committed on the step-length estimation and the determination of the motion direction. Moreover, several tests are performed by walking in urban and suburban environments: the results show that a suitable trade-off between energy-saving and position accuracy can be reached by switching the GNSS receiver on and off.

Highlights

Nowadays, thanks to the technological development and the mass diffusion of mobile devices, there is an increasing interest in those applications that allow users to keep track of their movements and provide location-based services
The position results that have been obtained by using only inertial data, and using the pedestrian dead reckoning (PDR) method, will be compared with those obtained from global navigation satellite systems (GNSS) data and the reconstruction of the Kalman filter (KF)
The urban environment is a typical scenario in which there may be disturbances on the Earth magnetic field given by vehicles and buildings

Summary

Introduction

Thanks to the technological development and the mass diffusion of mobile devices, there is an increasing interest in those applications that allow users to keep track of their movements and provide location-based services. Positioning and navigation solutions are generally provided by global navigation satellite systems (GNSS) These systems, together with the help of mobile networks, allow to obtain a position solution with bounded errors in a relatively short time, but its prolonged use leads to a rapid drain of the battery and its performance can strongly decrease in those environments that do not present an open sky visibility of the satellites. This is in contrast to the needs of users, whose interest is to have both the positioning service available in harsh environments such as the urban one and a battery life that can cover the whole day. By exploiting the computing resources of the processors and the low energy consumption of these sensors, some manufacturers may implement additional features in the devices such as fall detection and step counting, providing useful information on the user’s quality of life [1]

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