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Estimation of a Human-Maneuvered Target Incorporating Human Intention
This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intenti...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8399805/ https://www.ncbi.nlm.nih.gov/pubmed/34450757 http://dx.doi.org/10.3390/s21165316 |
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author | Qin, Yongming Kumon, Makoto Furukawa, Tomonari |
author_facet | Qin, Yongming Kumon, Makoto Furukawa, Tomonari |
author_sort | Qin, Yongming |
collection | PubMed |
description | This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intention-pattern model. The human intentions relate to labels of continuous states; the motion patterns characterize the change of continuous states. In the preprocessing, an Interacting Multiple Model (IMM) estimation technique is used to infer the intentions and extract motions, which eventually construct the intention-pattern model. Once the intention-pattern model has been constructed, the proposed approach incorporate the intention-pattern model to estimation using any state estimator including Kalman filter. The proposed approach not only estimates the mean using the human intention more accurately but also updates the covariance using the human intention more precisely. The performance of the proposed approach was investigated through the estimation of a human-maneuvered multirotor. The result of the application has first indicated the effectiveness of the proposed approach for constructing the intention-pattern model. The ability of the proposed approach in state estimation over the conventional technique without intention incorporation has then been demonstrated. |
format | Online Article Text |
id | pubmed-8399805 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83998052021-08-29 Estimation of a Human-Maneuvered Target Incorporating Human Intention Qin, Yongming Kumon, Makoto Furukawa, Tomonari Sensors (Basel) Article This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intention-pattern model. The human intentions relate to labels of continuous states; the motion patterns characterize the change of continuous states. In the preprocessing, an Interacting Multiple Model (IMM) estimation technique is used to infer the intentions and extract motions, which eventually construct the intention-pattern model. Once the intention-pattern model has been constructed, the proposed approach incorporate the intention-pattern model to estimation using any state estimator including Kalman filter. The proposed approach not only estimates the mean using the human intention more accurately but also updates the covariance using the human intention more precisely. The performance of the proposed approach was investigated through the estimation of a human-maneuvered multirotor. The result of the application has first indicated the effectiveness of the proposed approach for constructing the intention-pattern model. The ability of the proposed approach in state estimation over the conventional technique without intention incorporation has then been demonstrated. MDPI 2021-08-06 /pmc/articles/PMC8399805/ /pubmed/34450757 http://dx.doi.org/10.3390/s21165316 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Qin, Yongming Kumon, Makoto Furukawa, Tomonari Estimation of a Human-Maneuvered Target Incorporating Human Intention |
title | Estimation of a Human-Maneuvered Target Incorporating Human Intention |
title_full | Estimation of a Human-Maneuvered Target Incorporating Human Intention |
title_fullStr | Estimation of a Human-Maneuvered Target Incorporating Human Intention |
title_full_unstemmed | Estimation of a Human-Maneuvered Target Incorporating Human Intention |
title_short | Estimation of a Human-Maneuvered Target Incorporating Human Intention |
title_sort | estimation of a human-maneuvered target incorporating human intention |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8399805/ https://www.ncbi.nlm.nih.gov/pubmed/34450757 http://dx.doi.org/10.3390/s21165316 |
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