An Indoor Positioning Error Correction Method of Pedestrian Multi-Motions Recognized by Hybrid-Orders Fraction Domain Transformation

Aiming at the inherent problems of heading divergence and error accumulation in indoor inertial positioning of pedestrian field, we proposed a new indoor positioning error correction method for pedestrian multi-motions recognition. This method is aimed at pedestrians' seven common indoor moveme...

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Main Authors: Chao Li, Zhong Su, Qing Li, Hui Zhao
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8607989/
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spelling doaj-fbe3260aaa654bc3a363e39468e82df62021-03-29T22:01:23ZengIEEEIEEE Access2169-35362019-01-017113601137710.1109/ACCESS.2019.28915128607989An Indoor Positioning Error Correction Method of Pedestrian Multi-Motions Recognized by Hybrid-Orders Fraction Domain TransformationChao Li0https://orcid.org/0000-0002-0642-7334Zhong Su1Qing Li2Hui Zhao3https://orcid.org/0000-0002-5326-9930School of Automation, Beijing Institute of Technology, Beijing, ChinaSchool of Automation, Beijing Institute of Technology, Beijing, ChinaBeijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science and Technological University, Beijing, ChinaSchool of Automation, Beijing Institute of Technology, Beijing, ChinaAiming at the inherent problems of heading divergence and error accumulation in indoor inertial positioning of pedestrian field, we proposed a new indoor positioning error correction method for pedestrian multi-motions recognition. This method is aimed at pedestrians' seven common indoor movements. Periodically, divide the motion data of the accelerometer in the sensitive-axis-direction (vertical geodetic direction) of MEMS-IMU worn on the pedestrian's waist. And acquire the feature vector through feature extraction by the hybrid-orders fraction domain transformation. The effective features with high identification degree under the different optimal order transformations are mixed and matched. Then, these are sent to each sub-classifier to complete the classification process by the dichotomy and finish the processes of subsequent machine learning. The error correction of the heading angle and positioning is carried out combining with the improved HDE algorithm, the innovative floor constraints' method, and the motion states' transition correction technique. The final indoor positioning experiment results show that the classification effect of this motion detection method is better than the traditional methods'. The average classification accuracy can reach up to 97%. And the method reduces the computing requirements for hardware. By using the floor constraint method, the vertical height difference can achieve the effect of complete reset of the origin. The trajectory lines of the movement in the same floor are well displayed. At the same time, the best horizontal error positioning result is only 1.72 m during the total travel distance of 412.40 m (TTD ≈ 0.42%).https://ieeexplore.ieee.org/document/8607989/Pedestrian multi-motions recognitionMEMS-IMUHFDTmachine learningerror correction
collection DOAJ
language English
format Article
sources DOAJ
author Chao Li
Zhong Su
Qing Li
Hui Zhao
spellingShingle Chao Li
Zhong Su
Qing Li
Hui Zhao
An Indoor Positioning Error Correction Method of Pedestrian Multi-Motions Recognized by Hybrid-Orders Fraction Domain Transformation
IEEE Access
Pedestrian multi-motions recognition
MEMS-IMU
HFDT
machine learning
error correction
author_facet Chao Li
Zhong Su
Qing Li
Hui Zhao
author_sort Chao Li
title An Indoor Positioning Error Correction Method of Pedestrian Multi-Motions Recognized by Hybrid-Orders Fraction Domain Transformation
title_short An Indoor Positioning Error Correction Method of Pedestrian Multi-Motions Recognized by Hybrid-Orders Fraction Domain Transformation
title_full An Indoor Positioning Error Correction Method of Pedestrian Multi-Motions Recognized by Hybrid-Orders Fraction Domain Transformation
title_fullStr An Indoor Positioning Error Correction Method of Pedestrian Multi-Motions Recognized by Hybrid-Orders Fraction Domain Transformation
title_full_unstemmed An Indoor Positioning Error Correction Method of Pedestrian Multi-Motions Recognized by Hybrid-Orders Fraction Domain Transformation
title_sort indoor positioning error correction method of pedestrian multi-motions recognized by hybrid-orders fraction domain transformation
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description Aiming at the inherent problems of heading divergence and error accumulation in indoor inertial positioning of pedestrian field, we proposed a new indoor positioning error correction method for pedestrian multi-motions recognition. This method is aimed at pedestrians' seven common indoor movements. Periodically, divide the motion data of the accelerometer in the sensitive-axis-direction (vertical geodetic direction) of MEMS-IMU worn on the pedestrian's waist. And acquire the feature vector through feature extraction by the hybrid-orders fraction domain transformation. The effective features with high identification degree under the different optimal order transformations are mixed and matched. Then, these are sent to each sub-classifier to complete the classification process by the dichotomy and finish the processes of subsequent machine learning. The error correction of the heading angle and positioning is carried out combining with the improved HDE algorithm, the innovative floor constraints' method, and the motion states' transition correction technique. The final indoor positioning experiment results show that the classification effect of this motion detection method is better than the traditional methods'. The average classification accuracy can reach up to 97%. And the method reduces the computing requirements for hardware. By using the floor constraint method, the vertical height difference can achieve the effect of complete reset of the origin. The trajectory lines of the movement in the same floor are well displayed. At the same time, the best horizontal error positioning result is only 1.72 m during the total travel distance of 412.40 m (TTD ≈ 0.42%).
topic Pedestrian multi-motions recognition
MEMS-IMU
HFDT
machine learning
error correction
url https://ieeexplore.ieee.org/document/8607989/
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