Cargando…

Improving the Heading Accuracy in Indoor Pedestrian Navigation Based on a Decision Tree and Kalman Filter

In pedestrian inertial navigation, multi-sensor fusion is often used to obtain accurate heading estimates. As a widely distributed signal source, the geomagnetic field is convenient to provide sufficiently accurate heading angles. Unfortunately, there is a broad presence of artificial magnetic pertu...

Descripción completa

Detalles Bibliográficos
Autores principales: Hu, Guanghui, Zhang, Weizhi, Wan, Hong, Li, Xinxin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146404/
https://www.ncbi.nlm.nih.gov/pubmed/32178289
http://dx.doi.org/10.3390/s20061578
_version_ 1783520193605009408
author Hu, Guanghui
Zhang, Weizhi
Wan, Hong
Li, Xinxin
author_facet Hu, Guanghui
Zhang, Weizhi
Wan, Hong
Li, Xinxin
author_sort Hu, Guanghui
collection PubMed
description In pedestrian inertial navigation, multi-sensor fusion is often used to obtain accurate heading estimates. As a widely distributed signal source, the geomagnetic field is convenient to provide sufficiently accurate heading angles. Unfortunately, there is a broad presence of artificial magnetic perturbations in indoor environments, leading to difficulties in geomagnetic correction. In this paper, by analyzing the spatial distribution model of the magnetic interference field on the geomagnetic field, two quantitative features have been found to be crucial in distinguishing normal magnetic data from anomalies. By leveraging these two features and the classification and regression tree (CART) algorithm, we trained a decision tree that is capable of extracting magnetic data from distorted measurements. Furthermore, this well-trained decision tree can be used as a reject gate in a Kalman filter. By combining the decision tree and Kalman filter, a high-precision indoor pedestrian navigation system based on a magnetically assisted inertial system is proposed. This system is then validated in a real indoor environment, and the results show that our system delivers state-of-the-art positioning performance. Compared to other baseline algorithms, an improvement of over 70% in the positioning accuracy is achieved.
format Online
Article
Text
id pubmed-7146404
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-71464042020-04-15 Improving the Heading Accuracy in Indoor Pedestrian Navigation Based on a Decision Tree and Kalman Filter Hu, Guanghui Zhang, Weizhi Wan, Hong Li, Xinxin Sensors (Basel) Article In pedestrian inertial navigation, multi-sensor fusion is often used to obtain accurate heading estimates. As a widely distributed signal source, the geomagnetic field is convenient to provide sufficiently accurate heading angles. Unfortunately, there is a broad presence of artificial magnetic perturbations in indoor environments, leading to difficulties in geomagnetic correction. In this paper, by analyzing the spatial distribution model of the magnetic interference field on the geomagnetic field, two quantitative features have been found to be crucial in distinguishing normal magnetic data from anomalies. By leveraging these two features and the classification and regression tree (CART) algorithm, we trained a decision tree that is capable of extracting magnetic data from distorted measurements. Furthermore, this well-trained decision tree can be used as a reject gate in a Kalman filter. By combining the decision tree and Kalman filter, a high-precision indoor pedestrian navigation system based on a magnetically assisted inertial system is proposed. This system is then validated in a real indoor environment, and the results show that our system delivers state-of-the-art positioning performance. Compared to other baseline algorithms, an improvement of over 70% in the positioning accuracy is achieved. MDPI 2020-03-12 /pmc/articles/PMC7146404/ /pubmed/32178289 http://dx.doi.org/10.3390/s20061578 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hu, Guanghui
Zhang, Weizhi
Wan, Hong
Li, Xinxin
Improving the Heading Accuracy in Indoor Pedestrian Navigation Based on a Decision Tree and Kalman Filter
title Improving the Heading Accuracy in Indoor Pedestrian Navigation Based on a Decision Tree and Kalman Filter
title_full Improving the Heading Accuracy in Indoor Pedestrian Navigation Based on a Decision Tree and Kalman Filter
title_fullStr Improving the Heading Accuracy in Indoor Pedestrian Navigation Based on a Decision Tree and Kalman Filter
title_full_unstemmed Improving the Heading Accuracy in Indoor Pedestrian Navigation Based on a Decision Tree and Kalman Filter
title_short Improving the Heading Accuracy in Indoor Pedestrian Navigation Based on a Decision Tree and Kalman Filter
title_sort improving the heading accuracy in indoor pedestrian navigation based on a decision tree and kalman filter
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146404/
https://www.ncbi.nlm.nih.gov/pubmed/32178289
http://dx.doi.org/10.3390/s20061578
work_keys_str_mv AT huguanghui improvingtheheadingaccuracyinindoorpedestriannavigationbasedonadecisiontreeandkalmanfilter
AT zhangweizhi improvingtheheadingaccuracyinindoorpedestriannavigationbasedonadecisiontreeandkalmanfilter
AT wanhong improvingtheheadingaccuracyinindoorpedestriannavigationbasedonadecisiontreeandkalmanfilter
AT lixinxin improvingtheheadingaccuracyinindoorpedestriannavigationbasedonadecisiontreeandkalmanfilter