Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/92740
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Aeronautical and Aviation Engineering | en_US |
dc.creator | Zhang, G | en_US |
dc.creator | Xu, P | en_US |
dc.creator | Xu, H | en_US |
dc.creator | Hsu, LT | en_US |
dc.date.accessioned | 2022-05-16T09:07:29Z | - |
dc.date.available | 2022-05-16T09:07:29Z | - |
dc.identifier.issn | 1530-437X | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/92740 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Zhang, G., Xu, P., Xu, H., & Hsu, L. T. (2021). Prediction on the Urban GNSS Measurement Uncertainty Based on Deep Learning Networks With Long Short-Term Memory. IEEE Sensors Journal, 21(18), 20563-20577 is available at https://doi.org/10.1109/JSEN.2021.3098006 | en_US |
dc.subject | Deep learning | en_US |
dc.subject | GNSS | en_US |
dc.subject | LSTM | en_US |
dc.subject | Multipath | en_US |
dc.subject | Navigation | en_US |
dc.subject | Urban canyon | en_US |
dc.title | Prediction on the urban GNSS measurement uncertainty based on deep learning networks with long short-term memory | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 20563 | en_US |
dc.identifier.epage | 20577 | en_US |
dc.identifier.volume | 21 | en_US |
dc.identifier.issue | 18 | en_US |
dc.identifier.doi | 10.1109/JSEN.2021.3098006 | en_US |
dcterms.abstract | The GNSS performance could be significantly degraded by the interferences in an urban canyon, such as the blockage of the direct signal and the measurement error due to reflected signals. Such interferences can hardly be predicted by statistical or physical models, making urban GNSS positioning unable to achieve satisfactory accuracy. The deep learning networks, specializing in extracting abstract representations from data, may learn the representation about the GNSS measurement quality from existing measurements, which can be employed to predict the interferences in an urban area. In this study, we proposed a deep learning network architecture combining the conventional fully connected neural networks (FCNNs) and the long short-term memory (LSTM) networks, to predict the GNSS satellite visibility and pseudorange error based on GNSS measurement-level data. The performance of the proposed deep learning networks is evaluated by real experimental data in an urban area. It can predict the satellite visibility with 80.1% accuracy and predict the pseudorange errors with an average difference of 4.9 meters to the labeled errors. Experiments are conducted to investigate what representations have been learned from data by the proposed deep learning networks. Analysis results show that the LSTM layer within the proposed networks may contain representations about the environment, which affects the prediction behavior and can associate with the real environment information. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE sensors journal, 15 Sept. 2021, v. 21, no. 18, p. 20563-20577 | en_US |
dcterms.isPartOf | IEEE sensors journal | en_US |
dcterms.issued | 2021-09-15 | - |
dc.identifier.scopus | 2-s2.0-85111044608 | - |
dc.description.validate | 202205 bckw | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | AAE-0023 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Research Institute for Sustainable Urban Development, Hong Kong Polytechnic University | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 54444533 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Zhang_Prediction_Urban_Gnss.pdf | Pre-Published version | 2.29 MB | Adobe PDF | View/Open |
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