Road vehicle state estimation using low-cost GPS/INS
Assuming known vehicle parameters, this paper proposes an innovative integrated Kalman filter (IKF) scheme to estimate vehicle dynamics, in particular the sideslip, the heading and the longitudinal velocity. The IKF is compared with the 2DoF linear bicycle model, the triple Kalman filter (KF) and a...
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| Veröffentlicht in: | Mechanical systems and signal processing 2011-08, Vol.25 (6), p.1988-2004 |
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| container_end_page | 2004 |
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| container_issue | 6 |
| container_start_page | 1988 |
| container_title | Mechanical systems and signal processing |
| container_volume | 25 |
| creator | Leung, King Tin Whidborne, James F. Purdy, David Barber, Phil |
| description | Assuming known vehicle parameters, this paper proposes an innovative integrated Kalman filter (IKF) scheme to estimate vehicle dynamics, in particular the sideslip, the heading and the longitudinal velocity. The IKF is compared with the 2DoF linear bicycle model, the triple Kalman filter (KF) and a model-based KF (MKF) in a simulation environment. Simulation results show that the proposed IKF is superior to other KF designs (both Kinematic KF and MKF) on state estimation when tyre characteristics are within the linear region (i.e. manoeuvres below 55
kph). |
| doi_str_mv | 10.1016/j.ymssp.2010.08.003 |
| format | Article |
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kph).</description><subject>Computer simulation</subject><subject>Geographic information systems</subject><subject>Global Positioning System</subject><subject>GPS</subject><subject>INS</subject><subject>IPG CarMaker</subject><subject>Kalman filter</subject><subject>Kalman filters</subject><subject>Low-cost</subject><subject>Mechanical systems</subject><subject>Satellite navigation systems</subject><subject>State estimation</subject><subject>Vehicles</subject><issn>0888-3270</issn><issn>1096-1216</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNp9kDFPwzAUhC0EEqXwC1iyMSV9jh3HGRhQBaVSBYjCbCXOC7hK42K7Rf33uJSZ6UlPd6e7j5BrChkFKiarbL_2fpPlED8gMwB2QkYUKpHSnIpTMgIpZcryEs7JhfcrAKg4iBHhr7Zukx1-Gt1j4kMdMEEfzLoOxg7J1pvhI-ntd6qtD8nsZTmZPy0vyVlX9x6v_u6YvD_cv00f08XzbD69W6SacxFSVrfY6rLoKJdlxRmrYtmibaio867hkssCKOMNo1IWrGiAdkxomkOXN6VAZGNyc8zdOPu1jbXU2niNfV8PaLdeSRlHlAWropIdldpZ7x12auPiBrdXFNQBkVqpX0TqgEiBVBFRdN0eXRhH7Aw65bXBQWNrHOqgWmv-9f8AZ5hufA</recordid><startdate>20110801</startdate><enddate>20110801</enddate><creator>Leung, King Tin</creator><creator>Whidborne, James F.</creator><creator>Purdy, David</creator><creator>Barber, Phil</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20110801</creationdate><title>Road vehicle state estimation using low-cost GPS/INS</title><author>Leung, King Tin ; Whidborne, James F. ; Purdy, David ; Barber, Phil</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c446t-3adedc75f1487943391015db16a2fb484850134b3188535b01f36c120f2b76ee3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Computer simulation</topic><topic>Geographic information systems</topic><topic>Global Positioning System</topic><topic>GPS</topic><topic>INS</topic><topic>IPG CarMaker</topic><topic>Kalman filter</topic><topic>Kalman filters</topic><topic>Low-cost</topic><topic>Mechanical systems</topic><topic>Satellite navigation systems</topic><topic>State estimation</topic><topic>Vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Leung, King Tin</creatorcontrib><creatorcontrib>Whidborne, James F.</creatorcontrib><creatorcontrib>Purdy, David</creatorcontrib><creatorcontrib>Barber, Phil</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Mechanical systems and signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Leung, King Tin</au><au>Whidborne, James F.</au><au>Purdy, David</au><au>Barber, Phil</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Road vehicle state estimation using low-cost GPS/INS</atitle><jtitle>Mechanical systems and signal processing</jtitle><date>2011-08-01</date><risdate>2011</risdate><volume>25</volume><issue>6</issue><spage>1988</spage><epage>2004</epage><pages>1988-2004</pages><issn>0888-3270</issn><eissn>1096-1216</eissn><abstract>Assuming known vehicle parameters, this paper proposes an innovative integrated Kalman filter (IKF) scheme to estimate vehicle dynamics, in particular the sideslip, the heading and the longitudinal velocity. The IKF is compared with the 2DoF linear bicycle model, the triple Kalman filter (KF) and a model-based KF (MKF) in a simulation environment. Simulation results show that the proposed IKF is superior to other KF designs (both Kinematic KF and MKF) on state estimation when tyre characteristics are within the linear region (i.e. manoeuvres below 55
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| fulltext | fulltext |
| identifier | ISSN: 0888-3270 |
| ispartof | Mechanical systems and signal processing, 2011-08, Vol.25 (6), p.1988-2004 |
| issn | 0888-3270 1096-1216 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_889407539 |
| source | Elsevier ScienceDirect Journals |
| subjects | Computer simulation Geographic information systems Global Positioning System GPS INS IPG CarMaker Kalman filter Kalman filters Low-cost Mechanical systems Satellite navigation systems State estimation Vehicles |
| title | Road vehicle state estimation using low-cost GPS/INS |
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