An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data

Position-estimation systems for indoor localization play an important role in everyday life. The global positioning system (GPS) is a popular positioning system, which is mainly efficient for outdoor environments. In indoor scenarios, GPS signal reception is weak. Therefore, achieving good position...

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Autores principales:	Alwin Poulose, Odongo Steven Eyobu, Dong Seog Han
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2019
Materias:	Indoor positioning system (IPS) pedestrian dead reckoning (PDR) heading estimation indoor navigation Android-based smartphone quaternion Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/2d0de10815884da9974efa618495e82f
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id	oai:doaj.org-article:2d0de10815884da9974efa618495e82f
record_format	dspace
spelling	oai:doaj.org-article:2d0de10815884da9974efa618495e82f2021-11-19T00:02:16ZAn Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data2169-353610.1109/ACCESS.2019.2891942https://doaj.org/article/2d0de10815884da9974efa618495e82f2019-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/8606925/https://doaj.org/toc/2169-3536Position-estimation systems for indoor localization play an important role in everyday life. The global positioning system (GPS) is a popular positioning system, which is mainly efficient for outdoor environments. In indoor scenarios, GPS signal reception is weak. Therefore, achieving good position estimation accuracy is a challenge. To overcome this challenge, it is necessary to utilize other position-estimation systems for indoor localization. However, other existing indoor localization systems, especially based on inertial measurement unit (IMU) sensor data, still face challenges such as accumulated errors from sensors and external magnetic field effects. This paper proposes a position-estimation algorithm that uses the combined features of the accelerometer, magnetometer, and gyroscope data from an IMU sensor for position estimation. In this paper, we first estimate the pitch and roll values based on a fusion of accelerometer and gyroscope sensor values. The estimated pitch values are used for step detection. The step lengths are estimated by using the pitching amplitude. The heading of the pedestrian is estimated by the fusion of magnetometer and gyroscope sensor values. Finally, the position is estimated based on the step length and heading information. The proposed pitch-based step detection algorithm achieves 2.5% error as compared with acceleration-based step detection approaches. The heading estimation proposed in this paper achieves a mean heading error of 4.72° as compared with the azimuth- and magnetometer-based approaches. The experimental results show that the proposed position-estimation algorithm achieves a high position accuracy that significantly outperforms that of conventional estimation methods used for validation in this paper.Alwin PouloseOdongo Steven EyobuDong Seog HanIEEEarticleIndoor positioning system (IPS)pedestrian dead reckoning (PDR)heading estimationindoor navigationAndroid-based smartphonequaternionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 7, Pp 11165-11177 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Indoor positioning system (IPS) pedestrian dead reckoning (PDR) heading estimation indoor navigation Android-based smartphone quaternion Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Indoor positioning system (IPS) pedestrian dead reckoning (PDR) heading estimation indoor navigation Android-based smartphone quaternion Electrical engineering. Electronics. Nuclear engineering TK1-9971 Alwin Poulose Odongo Steven Eyobu Dong Seog Han An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data
description	Position-estimation systems for indoor localization play an important role in everyday life. The global positioning system (GPS) is a popular positioning system, which is mainly efficient for outdoor environments. In indoor scenarios, GPS signal reception is weak. Therefore, achieving good position estimation accuracy is a challenge. To overcome this challenge, it is necessary to utilize other position-estimation systems for indoor localization. However, other existing indoor localization systems, especially based on inertial measurement unit (IMU) sensor data, still face challenges such as accumulated errors from sensors and external magnetic field effects. This paper proposes a position-estimation algorithm that uses the combined features of the accelerometer, magnetometer, and gyroscope data from an IMU sensor for position estimation. In this paper, we first estimate the pitch and roll values based on a fusion of accelerometer and gyroscope sensor values. The estimated pitch values are used for step detection. The step lengths are estimated by using the pitching amplitude. The heading of the pedestrian is estimated by the fusion of magnetometer and gyroscope sensor values. Finally, the position is estimated based on the step length and heading information. The proposed pitch-based step detection algorithm achieves 2.5% error as compared with acceleration-based step detection approaches. The heading estimation proposed in this paper achieves a mean heading error of 4.72° as compared with the azimuth- and magnetometer-based approaches. The experimental results show that the proposed position-estimation algorithm achieves a high position accuracy that significantly outperforms that of conventional estimation methods used for validation in this paper.
format	article
author	Alwin Poulose Odongo Steven Eyobu Dong Seog Han
author_facet	Alwin Poulose Odongo Steven Eyobu Dong Seog Han
author_sort	Alwin Poulose
title	An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data
title_short	An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data
title_full	An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data
title_fullStr	An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data
title_full_unstemmed	An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data
title_sort	indoor position-estimation algorithm using smartphone imu sensor data
publisher	IEEE
publishDate	2019
url	https://doaj.org/article/2d0de10815884da9974efa618495e82f
work_keys_str_mv	AT alwinpoulose anindoorpositionestimationalgorithmusingsmartphoneimusensordata AT odongosteveneyobu anindoorpositionestimationalgorithmusingsmartphoneimusensordata AT dongseoghan anindoorpositionestimationalgorithmusingsmartphoneimusensordata AT alwinpoulose indoorpositionestimationalgorithmusingsmartphoneimusensordata AT odongosteveneyobu indoorpositionestimationalgorithmusingsmartphoneimusensordata AT dongseoghan indoorpositionestimationalgorithmusingsmartphoneimusensordata
_version_	1718420659927777280

An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data

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