Deep neural network‐based adaptive zero‐velocity detection for pedestrian navigation system.
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| Title: | Deep neural network‐based adaptive zero‐velocity detection for pedestrian navigation system. |
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| Authors: | Zhang, Liqiang1, Chen, Boxuan1, Li, Hu1, Liu, Yu1 liuyu@tju.edu.cn |
| Source: | Electronics Letters (Wiley-Blackwell). Jan2022, Vol. 58 Issue 1, p28-31. 4p. |
| Subjects: | Artificial neural networks, Phase detectors, Navigation, Pedestrians, Standard deviations |
| Abstract: | The zero‐velocity update (ZUPT) method is an effective way to reduce accumulated velocity errors of pedestrian navigation systems (PNSs). For a typical scheme, a stance phase detection module based on a fixed threshold is used to trigger the ZUPT algorithm. However, the detector is not robust enough for dynamic gait speeds. The false detection will degrade the navigation performance. In this letter, to improve the stance phase detector, the adaptive zero‐velocity detection problem is cast under dynamic gait speeds as a sequential threshold of a traditional detector inferring problem and a zero‐velocity detection framework proposed by combining a deep neural network with a traditional binary gait phase detector. Sufficient experimental results show that the proposed method outperforms other discussed learning‐based methods taking into account the trade‐off among model performance, structure, and size. Compared with the traditional method with a fixed threshold, the real‐world high‐dynamic positioning experiments show that this proposed method reduces the root mean squared error (RMSE) of absolute distance error by 48.7%, RMSE of start‐end error by 12.5%, and average RMSE of position error by 19.2%. [ABSTRACT FROM AUTHOR] |
| Copyright of Electronics Letters (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 154315290 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Deep neural network‐based adaptive zero‐velocity detection for pedestrian navigation system. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Liqiang%22">Zhang, Liqiang</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Chen%2C+Boxuan%22">Chen, Boxuan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Li%2C+Hu%22">Li, Hu</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Liu%2C+Yu%22">Liu, Yu</searchLink><relatesTo>1</relatesTo><i> liuyu@tju.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Electronics+Letters+%28Wiley-Blackwell%29%22">Electronics Letters (Wiley-Blackwell)</searchLink>. Jan2022, Vol. 58 Issue 1, p28-31. 4p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Phase+detectors%22">Phase detectors</searchLink><br /><searchLink fieldCode="DE" term="%22Navigation%22">Navigation</searchLink><br /><searchLink fieldCode="DE" term="%22Pedestrians%22">Pedestrians</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The zero‐velocity update (ZUPT) method is an effective way to reduce accumulated velocity errors of pedestrian navigation systems (PNSs). For a typical scheme, a stance phase detection module based on a fixed threshold is used to trigger the ZUPT algorithm. However, the detector is not robust enough for dynamic gait speeds. The false detection will degrade the navigation performance. In this letter, to improve the stance phase detector, the adaptive zero‐velocity detection problem is cast under dynamic gait speeds as a sequential threshold of a traditional detector inferring problem and a zero‐velocity detection framework proposed by combining a deep neural network with a traditional binary gait phase detector. Sufficient experimental results show that the proposed method outperforms other discussed learning‐based methods taking into account the trade‐off among model performance, structure, and size. Compared with the traditional method with a fixed threshold, the real‐world high‐dynamic positioning experiments show that this proposed method reduces the root mean squared error (RMSE) of absolute distance error by 48.7%, RMSE of start‐end error by 12.5%, and average RMSE of position error by 19.2%. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Electronics Letters (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1049/ell2.12339 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 4 StartPage: 28 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Phase detectors Type: general – SubjectFull: Navigation Type: general – SubjectFull: Pedestrians Type: general – SubjectFull: Standard deviations Type: general Titles: – TitleFull: Deep neural network‐based adaptive zero‐velocity detection for pedestrian navigation system. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Liqiang – PersonEntity: Name: NameFull: Chen, Boxuan – PersonEntity: Name: NameFull: Li, Hu – PersonEntity: Name: NameFull: Liu, Yu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 00135194 Numbering: – Type: volume Value: 58 – Type: issue Value: 1 Titles: – TitleFull: Electronics Letters (Wiley-Blackwell) Type: main |
| ResultId | 1 |
