3-D Displacement Detection Based on Enhanced Clustering From GNSS Positioning in a Kinematic Mode for Deformation Monitoring
For decades, displacement detection based on global navigation satellite system (GNSS) has increasingly been an important part of deformation monitoring for applications, such as dams, bridges, and high-rise buildings. Automatic identification and extraction of 3-D displacements from GNSS kinematic...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on instrumentation and measurement 2023, Vol.72, p.1-10 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 10 |
|---|---|
| container_issue | |
| container_start_page | 1 |
| container_title | IEEE transactions on instrumentation and measurement |
| container_volume | 72 |
| creator | Shen, Nan Wang, Bin Gao, Guiyun Chen, Liang Chen, Ruizhi |
| description | For decades, displacement detection based on global navigation satellite system (GNSS) has increasingly been an important part of deformation monitoring for applications, such as dams, bridges, and high-rise buildings. Automatic identification and extraction of 3-D displacements from GNSS kinematic positioning can provide a basis for emergency response decision-making and play a crucial role in natural and secondary disasters. However, due to the limitation of single epoch positioning accuracy, automatic detection of displacement from GNSS kinematic positioning results is still a challenge. To resolve this, we propose an enhanced K -means clustering method to detect displacements from GNSS kinematic positioning, which identifies the displacement by clustering and obtains displacements from adjacent clusters. Results from simulation and field experiments have demonstrated the effectiveness of the proposed method. The accuracy of 3-D displacement extraction from GNSS real-time kinematic (RTK) positioning can reach millimeter level. |
| doi_str_mv | 10.1109/TIM.2022.3223072 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_2766618648</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9954425</ieee_id><sourcerecordid>2766618648</sourcerecordid><originalsourceid>FETCH-LOGICAL-c291t-1119a91fa84028eac3b67baab917f24f811c64a2df35d30f49c0e9fea57cb33</originalsourceid><addsrcrecordid>eNo9kM1LAzEQxYMoWKt3wUvA89Z8bXZz1H5ZbFVo70s2nWhKd1OT7UHwjzdLxcvM8Pi9N_AQuqVkRClRD5vFasQIYyPOGCcFO0MDmudFpqRk52hACC0zJXJ5ia5i3BFCCimKAfrh2QRPXDzstYEG2g5PoAPTOd_iJx1hi9MxbT91a9I93h9jB8G1H3gWfIPnr-s1fvfR9XyvuhZr_OJaaHTnDF75LWDrQwpNs9dS2iqhne9DrtGF1fsIN397iNaz6Wb8nC3f5ovx4zIzTNEuo5QqrajVpSCsBG14LYta61rRwjJhS0qNFJptLc-3nFihDAFlQeeFqTkfovtT6iH4ryPErtr5Y2jTw4oVUkpaSlEmipwoE3yMAWx1CK7R4buipOobrlLDVd9w9ddwstydLA4A_nGlciFYzn8Bx2Z3wQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2766618648</pqid></control><display><type>article</type><title>3-D Displacement Detection Based on Enhanced Clustering From GNSS Positioning in a Kinematic Mode for Deformation Monitoring</title><source>IEEE Electronic Library (IEL)</source><creator>Shen, Nan ; Wang, Bin ; Gao, Guiyun ; Chen, Liang ; Chen, Ruizhi</creator><creatorcontrib>Shen, Nan ; Wang, Bin ; Gao, Guiyun ; Chen, Liang ; Chen, Ruizhi</creatorcontrib><description>For decades, displacement detection based on global navigation satellite system (GNSS) has increasingly been an important part of deformation monitoring for applications, such as dams, bridges, and high-rise buildings. Automatic identification and extraction of 3-D displacements from GNSS kinematic positioning can provide a basis for emergency response decision-making and play a crucial role in natural and secondary disasters. However, due to the limitation of single epoch positioning accuracy, automatic detection of displacement from GNSS kinematic positioning results is still a challenge. To resolve this, we propose an enhanced <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-means clustering method to detect displacements from GNSS kinematic positioning, which identifies the displacement by clustering and obtains displacements from adjacent clusters. Results from simulation and field experiments have demonstrated the effectiveness of the proposed method. The accuracy of 3-D displacement extraction from GNSS real-time kinematic (RTK) positioning can reach millimeter level.</description><identifier>ISSN: 0018-9456</identifier><identifier>EISSN: 1557-9662</identifier><identifier>DOI: 10.1109/TIM.2022.3223072</identifier><identifier>CODEN: IEIMAO</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Accuracy ; Bridges ; Clustering ; Decision making ; Deformation ; Displacement ; Displacement detection ; Emergency response ; gap statistic ; Global navigation satellite system ; global navigation satellite system (GNSS) ; High rise buildings ; K-means clustering ; Kinematics ; local outlier factor (LOF) ; Monitoring ; real-time kinematic (RTK) positioning ; Real-time systems ; Satellites ; Strain</subject><ispartof>IEEE transactions on instrumentation and measurement, 2023, Vol.72, p.1-10</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-1119a91fa84028eac3b67baab917f24f811c64a2df35d30f49c0e9fea57cb33</citedby><cites>FETCH-LOGICAL-c291t-1119a91fa84028eac3b67baab917f24f811c64a2df35d30f49c0e9fea57cb33</cites><orcidid>0000-0001-5609-1149 ; 0000-0001-8468-5550 ; 0000-0002-7083-6001 ; 0000-0003-2769-8907 ; 0000-0001-6683-2342</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9954425$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,4010,27900,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9954425$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Shen, Nan</creatorcontrib><creatorcontrib>Wang, Bin</creatorcontrib><creatorcontrib>Gao, Guiyun</creatorcontrib><creatorcontrib>Chen, Liang</creatorcontrib><creatorcontrib>Chen, Ruizhi</creatorcontrib><title>3-D Displacement Detection Based on Enhanced Clustering From GNSS Positioning in a Kinematic Mode for Deformation Monitoring</title><title>IEEE transactions on instrumentation and measurement</title><addtitle>TIM</addtitle><description>For decades, displacement detection based on global navigation satellite system (GNSS) has increasingly been an important part of deformation monitoring for applications, such as dams, bridges, and high-rise buildings. Automatic identification and extraction of 3-D displacements from GNSS kinematic positioning can provide a basis for emergency response decision-making and play a crucial role in natural and secondary disasters. However, due to the limitation of single epoch positioning accuracy, automatic detection of displacement from GNSS kinematic positioning results is still a challenge. To resolve this, we propose an enhanced <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-means clustering method to detect displacements from GNSS kinematic positioning, which identifies the displacement by clustering and obtains displacements from adjacent clusters. Results from simulation and field experiments have demonstrated the effectiveness of the proposed method. The accuracy of 3-D displacement extraction from GNSS real-time kinematic (RTK) positioning can reach millimeter level.</description><subject>Accuracy</subject><subject>Bridges</subject><subject>Clustering</subject><subject>Decision making</subject><subject>Deformation</subject><subject>Displacement</subject><subject>Displacement detection</subject><subject>Emergency response</subject><subject>gap statistic</subject><subject>Global navigation satellite system</subject><subject>global navigation satellite system (GNSS)</subject><subject>High rise buildings</subject><subject>K-means clustering</subject><subject>Kinematics</subject><subject>local outlier factor (LOF)</subject><subject>Monitoring</subject><subject>real-time kinematic (RTK) positioning</subject><subject>Real-time systems</subject><subject>Satellites</subject><subject>Strain</subject><issn>0018-9456</issn><issn>1557-9662</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kM1LAzEQxYMoWKt3wUvA89Z8bXZz1H5ZbFVo70s2nWhKd1OT7UHwjzdLxcvM8Pi9N_AQuqVkRClRD5vFasQIYyPOGCcFO0MDmudFpqRk52hACC0zJXJ5ia5i3BFCCimKAfrh2QRPXDzstYEG2g5PoAPTOd_iJx1hi9MxbT91a9I93h9jB8G1H3gWfIPnr-s1fvfR9XyvuhZr_OJaaHTnDF75LWDrQwpNs9dS2iqhne9DrtGF1fsIN397iNaz6Wb8nC3f5ovx4zIzTNEuo5QqrajVpSCsBG14LYta61rRwjJhS0qNFJptLc-3nFihDAFlQeeFqTkfovtT6iH4ryPErtr5Y2jTw4oVUkpaSlEmipwoE3yMAWx1CK7R4buipOobrlLDVd9w9ddwstydLA4A_nGlciFYzn8Bx2Z3wQ</recordid><startdate>2023</startdate><enddate>2023</enddate><creator>Shen, Nan</creator><creator>Wang, Bin</creator><creator>Gao, Guiyun</creator><creator>Chen, Liang</creator><creator>Chen, Ruizhi</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0001-5609-1149</orcidid><orcidid>https://orcid.org/0000-0001-8468-5550</orcidid><orcidid>https://orcid.org/0000-0002-7083-6001</orcidid><orcidid>https://orcid.org/0000-0003-2769-8907</orcidid><orcidid>https://orcid.org/0000-0001-6683-2342</orcidid></search><sort><creationdate>2023</creationdate><title>3-D Displacement Detection Based on Enhanced Clustering From GNSS Positioning in a Kinematic Mode for Deformation Monitoring</title><author>Shen, Nan ; Wang, Bin ; Gao, Guiyun ; Chen, Liang ; Chen, Ruizhi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-1119a91fa84028eac3b67baab917f24f811c64a2df35d30f49c0e9fea57cb33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Bridges</topic><topic>Clustering</topic><topic>Decision making</topic><topic>Deformation</topic><topic>Displacement</topic><topic>Displacement detection</topic><topic>Emergency response</topic><topic>gap statistic</topic><topic>Global navigation satellite system</topic><topic>global navigation satellite system (GNSS)</topic><topic>High rise buildings</topic><topic>K-means clustering</topic><topic>Kinematics</topic><topic>local outlier factor (LOF)</topic><topic>Monitoring</topic><topic>real-time kinematic (RTK) positioning</topic><topic>Real-time systems</topic><topic>Satellites</topic><topic>Strain</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shen, Nan</creatorcontrib><creatorcontrib>Wang, Bin</creatorcontrib><creatorcontrib>Gao, Guiyun</creatorcontrib><creatorcontrib>Chen, Liang</creatorcontrib><creatorcontrib>Chen, Ruizhi</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on instrumentation and measurement</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Shen, Nan</au><au>Wang, Bin</au><au>Gao, Guiyun</au><au>Chen, Liang</au><au>Chen, Ruizhi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>3-D Displacement Detection Based on Enhanced Clustering From GNSS Positioning in a Kinematic Mode for Deformation Monitoring</atitle><jtitle>IEEE transactions on instrumentation and measurement</jtitle><stitle>TIM</stitle><date>2023</date><risdate>2023</risdate><volume>72</volume><spage>1</spage><epage>10</epage><pages>1-10</pages><issn>0018-9456</issn><eissn>1557-9662</eissn><coden>IEIMAO</coden><abstract>For decades, displacement detection based on global navigation satellite system (GNSS) has increasingly been an important part of deformation monitoring for applications, such as dams, bridges, and high-rise buildings. Automatic identification and extraction of 3-D displacements from GNSS kinematic positioning can provide a basis for emergency response decision-making and play a crucial role in natural and secondary disasters. However, due to the limitation of single epoch positioning accuracy, automatic detection of displacement from GNSS kinematic positioning results is still a challenge. To resolve this, we propose an enhanced <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-means clustering method to detect displacements from GNSS kinematic positioning, which identifies the displacement by clustering and obtains displacements from adjacent clusters. Results from simulation and field experiments have demonstrated the effectiveness of the proposed method. The accuracy of 3-D displacement extraction from GNSS real-time kinematic (RTK) positioning can reach millimeter level.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TIM.2022.3223072</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-5609-1149</orcidid><orcidid>https://orcid.org/0000-0001-8468-5550</orcidid><orcidid>https://orcid.org/0000-0002-7083-6001</orcidid><orcidid>https://orcid.org/0000-0003-2769-8907</orcidid><orcidid>https://orcid.org/0000-0001-6683-2342</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0018-9456 |
| ispartof | IEEE transactions on instrumentation and measurement, 2023, Vol.72, p.1-10 |
| issn | 0018-9456 1557-9662 |
| language | eng |
| recordid | cdi_proquest_journals_2766618648 |
| source | IEEE Electronic Library (IEL) |
| subjects | Accuracy Bridges Clustering Decision making Deformation Displacement Displacement detection Emergency response gap statistic Global navigation satellite system global navigation satellite system (GNSS) High rise buildings K-means clustering Kinematics local outlier factor (LOF) Monitoring real-time kinematic (RTK) positioning Real-time systems Satellites Strain |
| title | 3-D Displacement Detection Based on Enhanced Clustering From GNSS Positioning in a Kinematic Mode for Deformation Monitoring |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-02T23%3A27%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=3-D%20Displacement%20Detection%20Based%20on%20Enhanced%20Clustering%20From%20GNSS%20Positioning%20in%20a%20Kinematic%20Mode%20for%20Deformation%20Monitoring&rft.jtitle=IEEE%20transactions%20on%20instrumentation%20and%20measurement&rft.au=Shen,%20Nan&rft.date=2023&rft.volume=72&rft.spage=1&rft.epage=10&rft.pages=1-10&rft.issn=0018-9456&rft.eissn=1557-9662&rft.coden=IEIMAO&rft_id=info:doi/10.1109/TIM.2022.3223072&rft_dat=%3Cproquest_RIE%3E2766618648%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2766618648&rft_id=info:pmid/&rft_ieee_id=9954425&rfr_iscdi=true |