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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/16/5316 |
| _version_ | 1797522091518459904 |
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| author | Yongming Qin Makoto Kumon Tomonari Furukawa |
| author_facet | Yongming Qin Makoto Kumon Tomonari Furukawa |
| author_sort | Yongming Qin |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T08:24:34Z |
| format | Article |
| id | doaj.art-9838727257074741ba74a24e4aaeacad |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T08:24:34Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9838727257074741ba74a24e4aaeacad2023-11-22T09:37:36ZengMDPI AGSensors1424-82202021-08-012116531610.3390/s21165316Estimation of a Human-Maneuvered Target Incorporating Human IntentionYongming Qin0Makoto Kumon1Tomonari Furukawa2Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22903, USAFaculty of Advanced Science and Technology, International Research Organization of Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, Kumamoto 860-8555, JapanDepartment of Mechanical Engineering, University of Virginia, Charlottesville, VA 22903, USAThis 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.https://www.mdpi.com/1424-8220/21/16/5316estimationtrackinghuman intentionmotion patternpredictionmultiple model |
| spellingShingle | Yongming Qin Makoto Kumon Tomonari Furukawa Estimation of a Human-Maneuvered Target Incorporating Human Intention Sensors estimation tracking human intention motion pattern prediction multiple model |
| 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 | estimation tracking human intention motion pattern prediction multiple model |
| url | https://www.mdpi.com/1424-8220/21/16/5316 |
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