Intelligent Localization of a High-Speed Train Using LSSVM and the Online Sparse Optimization Approach

For a high-speed train (HST), quick and accurate localization of its position is crucial to safe and effective operation of the HST. In this paper, we develop a mathematical localization model by analyzing the location report created by the HST. Then, we apply two sparse optimization algorithms, i.e...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on intelligent transportation systems Jg. 18; H. 8; S. 2071 - 2084
Hauptverfasser:	Cheng, Ruijun, Song, Yongduan, Chen, Dewang, Chen, Long
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Algorithms Computational modeling Computer memory Data models Distance learning High speed High speed rail High-speed train Iterative methods iterative pruning error minimization Localization location error LSSVM L₀-norm minimization Machine learning Mathematical model online sparse optimization Optimization Pruning Rail transportation Real time Real-time systems Support vector machines
ISSN:	1524-9050, 1558-0016
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	For a high-speed train (HST), quick and accurate localization of its position is crucial to safe and effective operation of the HST. In this paper, we develop a mathematical localization model by analyzing the location report created by the HST. Then, we apply two sparse optimization algorithms, i.e., iterative pruning error minimization (IPEM) and L 0 -norm minimization algorithms, to improve the sparsity of both least squares support vector machine (LSSVM) and weighted LSSVM models. Furthermore, in order to enhance the adaptability and real-time performance of established localization model, four online sparse learning algorithms LSSVM-online, IPEM-online, L 0 -norm-online, and hybrid-online are developed to sparsify the training data set and update parameters of the LSSVM model online. Finally, the field data of the Beijing-Shanghai highspeed railway (BS_HSR) is used to test the performance of the established localization models. The proposed method overcomes the problem of memory constraints and high computational costs resulting in highly sparse reductions to the LSSVM models. Experiments on real-world data sets from the BS_HSR illustrate that these methods achieve sparse models and increase the realtime performance in online updating process on the premise of reducing the location error. For the rapid convergence of proposed online sparse algorithms, the localization model can be updated when the HST passes through the balise every time.
AbstractList	For a high-speed train (HST), quick and accurate localization of its position is crucial to safe and effective operation of the HST. In this paper, we develop a mathematical localization model by analyzing the location report created by the HST. Then, we apply two sparse optimization algorithms, i.e., iterative pruning error minimization (IPEM) and [Formula Omitted]-norm minimization algorithms, to improve the sparsity of both least squares support vector machine (LSSVM) and weighted LSSVM models. Furthermore, in order to enhance the adaptability and real-time performance of established localization model, four online sparse learning algorithms LSSVM-online, IPEM-online, [Formula Omitted]-norm-online, and hybrid-online are developed to sparsify the training data set and update parameters of the LSSVM model online. Finally, the field data of the Beijing–Shanghai high-speed railway (BS_HSR) is used to test the performance of the established localization models. The proposed method overcomes the problem of memory constraints and high computational costs resulting in highly sparse reductions to the LSSVM models. Experiments on real-world data sets from the BS_HSR illustrate that these methods achieve sparse models and increase the real-time performance in online updating process on the premise of reducing the location error. For the rapid convergence of proposed online sparse algorithms, the localization model can be updated when the HST passes through the balise every time. For a high-speed train (HST), quick and accurate localization of its position is crucial to safe and effective operation of the HST. In this paper, we develop a mathematical localization model by analyzing the location report created by the HST. Then, we apply two sparse optimization algorithms, i.e., iterative pruning error minimization (IPEM) and L 0 -norm minimization algorithms, to improve the sparsity of both least squares support vector machine (LSSVM) and weighted LSSVM models. Furthermore, in order to enhance the adaptability and real-time performance of established localization model, four online sparse learning algorithms LSSVM-online, IPEM-online, L 0 -norm-online, and hybrid-online are developed to sparsify the training data set and update parameters of the LSSVM model online. Finally, the field data of the Beijing-Shanghai highspeed railway (BS_HSR) is used to test the performance of the established localization models. The proposed method overcomes the problem of memory constraints and high computational costs resulting in highly sparse reductions to the LSSVM models. Experiments on real-world data sets from the BS_HSR illustrate that these methods achieve sparse models and increase the realtime performance in online updating process on the premise of reducing the location error. For the rapid convergence of proposed online sparse algorithms, the localization model can be updated when the HST passes through the balise every time.
Author	Ruijun Cheng Dewang Chen Long Chen Yongduan Song
Author_xml	– sequence: 1 givenname: Ruijun orcidid: 0000-0003-0184-5446 surname: Cheng fullname: Cheng, Ruijun – sequence: 2 givenname: Yongduan surname: Song fullname: Song, Yongduan – sequence: 3 givenname: Dewang surname: Chen fullname: Chen, Dewang – sequence: 4 givenname: Long surname: Chen fullname: Chen, Long
BookMark	eNp9UD1PwzAUtFCRKIUfgFgsMafYsR3HY1UBrVTUISlr5DhO6yp1QuwO8OtxaGFgYHofunt3767ByLZWA3CH0RRjJB7zZZ5NY4STaZwQQii9AGPMWBqhsBsNfUwjgRi6AtfO7cOWMozHoF5ar5vGbLX1cNUq2ZhP6U1rYVtDCRdmu4uyTusK5r00Fm6csVu4yrK3VyhtBf1Ow7VtjNUw62TvwtR5c_g5Muu6vpVqdwMua9k4fXuuE7B5fsrni2i1flnOZ6tIxYL4qExKLjCvKYkVERXXopQUKYFqxqSoSZ1UKeKp1jhFjFZKhjeSUlWUlgmKWUUm4OF0N8i-H7Xzxb499jZIFljECRec4Tig-Aml-ta5XteFMv7bsA9PNgVGxRBqMYRaDKEW51ADE_9hdr05yP7jX879iWO01r94ngYrlJMvPtqEOQ
CODEN	ITISFG
CitedBy_id	crossref_primary_10_1016_j_iswa_2022_200087 crossref_primary_10_3390_rs16050809 crossref_primary_10_1016_j_ins_2020_10_018 crossref_primary_10_1109_TITS_2023_3332348 crossref_primary_10_23919_JSEE_2023_000103 crossref_primary_10_1016_j_future_2018_03_041 crossref_primary_10_1016_j_jfranklin_2023_06_029 crossref_primary_10_1109_TITS_2023_3305074 crossref_primary_10_1109_LRA_2019_2897169 crossref_primary_10_1109_LRA_2022_3190093 crossref_primary_10_1109_TITS_2023_3344468 crossref_primary_10_3390_s25154637 crossref_primary_10_1109_TITS_2024_3513717 crossref_primary_10_1109_JSEN_2025_3531643 crossref_primary_10_1109_TITS_2018_2878442 crossref_primary_10_1109_TIM_2018_2878073 crossref_primary_10_3390_e24030402 crossref_primary_10_1109_TCYB_2019_2915191 crossref_primary_10_1109_TETCI_2023_3349183 crossref_primary_10_1016_j_cie_2018_03_022 crossref_primary_10_3390_app14041552
Cites_doi	10.1016/j.asoc.2015.07.027 10.1016/j.ress.2015.09.014 10.1016/j.engappai.2016.10.002 10.1016/j.eswa.2011.08.002 10.1016/j.ress.2015.02.008 10.1016/j.asoc.2013.04.020 10.1109/TNN.2005.852239 10.3390/s131217130 10.1016/j.asoc.2009.08.025 10.1023/A:1009715923555 10.1016/S0925-2312(01)00644-0 10.1109/TITS.2013.2265171 10.1007/978-3-540-30499-9_194 10.1016/j.asoc.2015.01.017 10.1109/TCYB.2016.2521428 10.1023/B:STCO.0000035301.49549.88 10.1016/j.neucom.2014.02.036 10.1109/72.248452 10.1016/j.csda.2010.01.024 10.1016/j.knosys.2015.01.006 10.1007/s00521-015-1960-6 10.1007/s11063-010-9162-9 10.1023/A:1018628609742 10.1109/TNN.2003.810597 10.1016/j.trb.2016.05.009 10.1016/j.patrec.2010.06.017 10.1007/3-540-46084-5_116 10.1016/j.asoc.2006.04.002 10.1007/s00521-009-0258-y
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017
DBID	97E RIA RIE AAYXX CITATION 7SC 7SP 8FD FR3 JQ2 KR7 L7M L~C L~D
DOI	10.1109/TITS.2016.2633344
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Civil Engineering Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional
DatabaseTitleList	Civil Engineering Abstracts
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1558-0016
EndPage	2084
ExternalDocumentID	10_1109_TITS_2016_2633344 7875147
Genre	orig-research
GrantInformation_xml	– fundername: National Science Foundation of China grantid: 61103153/F020503 funderid: 10.13039/501100001809 – fundername: Start Project for Minjiang Chair Professor by Fujian Province grantid: 510146 – fundername: Project from the Fujian Province Key Laboratory of Network Computing and Intelligent Information Processing grantid: 2009J1007
GroupedDBID	-~X 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT AENEX AETIX AGQYO AGSQL AHBIQ AIBXA AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD HZ~ H~9 IFIPE IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RNS ZY4 AAYXX CITATION 7SC 7SP 8FD FR3 JQ2 KR7 L7M L~C L~D
ID	FETCH-LOGICAL-c293t-b6b7917f432c39d7e9ba40c90f55a9f3f6d8078ee18054dca0146bcd44b6025d3
IEDL.DBID	RIE
ISICitedReferencesCount	27
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000407347300006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1524-9050
IngestDate	Mon Jun 30 05:14:17 EDT 2025 Tue Nov 18 21:25:32 EST 2025 Sat Nov 29 06:34:51 EST 2025 Tue Aug 26 16:56:45 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	8
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c293t-b6b7917f432c39d7e9ba40c90f55a9f3f6d8078ee18054dca0146bcd44b6025d3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-0184-5446
PQID	1926797512
PQPubID	75735
PageCount	14
ParticipantIDs	crossref_citationtrail_10_1109_TITS_2016_2633344 crossref_primary_10_1109_TITS_2016_2633344 proquest_journals_1926797512 ieee_primary_7875147
PublicationCentury	2000
PublicationDate	2017-08-01
PublicationDateYYYYMMDD	2017-08-01
PublicationDate_xml	– month: 08 year: 2017 text: 2017-08-01 day: 01
PublicationDecade	2010
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on intelligent transportation systems
PublicationTitleAbbrev	TITS
PublicationYear	2017
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref34 ref12 ref15 ref14 ref30 ref33 ref32 ref10 ref2 ref1 ref17 ref19 weston (ref28) 2003; 3 ref18 chen (ref11) 2015; 30 vapnik (ref6) 1998 ref24 ref23 ref25 ref20 ref22 ref21 ref27 williams (ref16) 2001 ref29 ref8 ref7 (ref31) 2008 brabanter (ref35) 2009 ref9 ref4 ref3 vapnik (ref5) 1996 wang (ref26) 2013; 13
References_xml	– ident: ref12 doi: 10.1016/j.asoc.2015.07.027 – ident: ref2 doi: 10.1016/j.ress.2015.09.014 – year: 1998 ident: ref6 publication-title: Statistical Learning Theory – ident: ref33 doi: 10.1016/j.engappai.2016.10.002 – ident: ref3 doi: 10.1016/j.eswa.2011.08.002 – ident: ref1 doi: 10.1016/j.ress.2015.02.008 – year: 2008 ident: ref31 publication-title: Science and Technology Department of Chinese Ministry of Railways System Requirement Specification of Level-3 Chinese Train Control System (CTCS) – ident: ref4 doi: 10.1016/j.asoc.2013.04.020 – ident: ref20 doi: 10.1109/TNN.2005.852239 – volume: 13 start-page: 17130 year: 2013 ident: ref26 article-title: Online least squares one-class support vector machines-based abnormal visual event detection publication-title: SENSORS doi: 10.3390/s131217130 – ident: ref15 doi: 10.1016/j.asoc.2009.08.025 – ident: ref7 doi: 10.1023/A:1009715923555 – ident: ref24 doi: 10.1016/S0925-2312(01)00644-0 – ident: ref30 doi: 10.1109/TITS.2013.2265171 – ident: ref17 doi: 10.1007/978-3-540-30499-9_194 – volume: 30 start-page: 758 year: 2015 ident: ref11 article-title: Position computation models for high-speed train based on support vector machine approach publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2015.01.017 – ident: ref23 doi: 10.1109/TCYB.2016.2521428 – ident: ref8 doi: 10.1023/B:STCO.0000035301.49549.88 – ident: ref27 doi: 10.1016/j.neucom.2014.02.036 – year: 1996 ident: ref5 publication-title: The Nature of Statistical Learning Theory[M – ident: ref18 doi: 10.1109/72.248452 – ident: ref19 doi: 10.1016/j.csda.2010.01.024 – ident: ref22 doi: 10.1016/j.knosys.2015.01.006 – start-page: 682 year: 2001 ident: ref16 article-title: Using the nyström method to speed up kernel machines publication-title: Proc 14th Annu Conf Neural Inf Process Syst – ident: ref10 doi: 10.1007/s00521-015-1960-6 – ident: ref21 doi: 10.1007/s11063-010-9162-9 – start-page: 100 year: 2009 ident: ref35 article-title: Robustness of kernel based regression: A comparison of iterative weighting schemes publication-title: Proc Int Conf Artif Neural Netw (ICANN 1998) – volume: 3 start-page: 1439 year: 2003 ident: ref28 article-title: Use of the zero-norm with linear models and kernel methods publication-title: J Mach Learn Res – ident: ref13 doi: 10.1023/A:1018628609742 – ident: ref25 doi: 10.1109/TNN.2003.810597 – ident: ref32 doi: 10.1016/j.trb.2016.05.009 – ident: ref29 doi: 10.1016/j.patrec.2010.06.017 – ident: ref34 doi: 10.1007/3-540-46084-5_116 – ident: ref14 doi: 10.1016/j.asoc.2006.04.002 – ident: ref9 doi: 10.1007/s00521-009-0258-y
SSID	ssj0014511
Score	2.3305366
Snippet	For a high-speed train (HST), quick and accurate localization of its position is crucial to safe and effective operation of the HST. In this paper, we develop...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	2071
SubjectTerms	Adaptation models Algorithms Computational modeling Computer memory Data models Distance learning High speed High speed rail High-speed train Iterative methods iterative pruning error minimization Localization location error LSSVM L₀-norm minimization Machine learning Mathematical model online sparse optimization Optimization Pruning Rail transportation Real time Real-time systems Support vector machines
Title	Intelligent Localization of a High-Speed Train Using LSSVM and the Online Sparse Optimization Approach
URI	https://ieeexplore.ieee.org/document/7875147 https://www.proquest.com/docview/1926797512
Volume	18
WOSCitedRecordID	wos000407347300006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-0016 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014511 issn: 1524-9050 databaseCode: RIE dateStart: 20000101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fS8MwED624YM--GuK0yl58Ens1jVZ0zwOURzMIbSOvZU0SUHQbeyHf7-5rC2KIvjW0rSUfmm-u9zddwDXuLXPncKl4tKzC17mydxkXiRVpJFkqQu0T0Z8PI6mU_Fcg9uqFsYY45LPTAcPXSxfz9UGt8q6dnJZfud1qHMebmu1qogB6mw5bdSAecLvlxHMni-6yTCJMYkr7AQhpZSxbxzkmqr8WIkdvTwc_O_FDmG_MCPJYIv7EdTM7Bj2vogLNiEfVmqbazJCyipKLsk8J5JggocXLyx5kQTbRBCXPEBGcTx5InKmibUMyVaIlMQL6_7aM7u8vJcPGRRi5Cfw8nCf3D16RVcFT1lqX3tZmHHro-WMBooKzY3IJPOV8PN-X4qc5qFGDXpjepE157SSKC-TKc1YFlrsND2Fxmw-M2dAqPYjk3HrslDNekxKHmpL-FjsizW3ugV--Z1TVUiOY-eLt9S5Hr5IEZoUoUkLaFpwU92y2Opt_DW4iVhUAwsYWtAuwUyLP3KVWks25MJeD85_v-sCdgOkbJfc14bGerkxl7CjPtavq-WVm2yfnhDQAg
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fS8MwED78BeqDv6Y4nZoHn8RuXZM1zeMQxWEdQuvYW0mTFATdhlb_fnNZVxRF8K2laSn90nx3ubvvAM5xa587hUvFpWcXvNyThcm9SKpII8lSF2gfxXw4jMZj8bAEl3UtjDHGJZ-ZNh66WL6eqnfcKuvYyWX5nS_DKnbO6s2rteqYASptOXXUgHnC7y1imF1fdNJBmmAaV9gOQkopY99YyLVV-bEWO4K52f7fq-3AVmVIkv4c-V1YMpM92PwiL9iAYlDrbZYkRtKqii7JtCCSYIqHl8wsfZEUG0UQlz5A4iQZ3RM50cTahmQuRUqSmXWA7ZldYF4WD-lXcuT78HhznV7delVfBU9Zci-9PMy59dIKRgNFheZG5JL5SvhFrydFQYtQowq9Md3IGnRaSRSYyZVmLA8tepoewMpkOjGHQKj2I5Nz67RQzbpMSh5qS_lY7otVt7oJ_uI7Z6oSHcfeF8-Zcz58kSE0GUKTVdA04aK-ZTZX3PhrcAOxqAdWMDShtQAzq_7Jt8zasiEX9npw9PtdZ7B-m97HWTwY3h3DRoAE7lL9WrBSvr6bE1hTH-XT2-upm3ifo5PTTQ
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Intelligent+Localization+of+a+High-Speed+Train+Using+LSSVM+and+the+Online+Sparse+Optimization+Approach&rft.jtitle=IEEE+transactions+on+intelligent+transportation+systems&rft.au=Cheng%2C+Ruijun&rft.au=Song%2C+Yongduan&rft.au=Chen%2C+Dewang&rft.au=Long%2C+Chen&rft.date=2017-08-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=1524-9050&rft.eissn=1558-0016&rft.volume=18&rft.issue=8&rft.spage=2071&rft_id=info:doi/10.1109%2FTITS.2016.2633344&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1524-9050&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1524-9050&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1524-9050&client=summon