Kalman Filter-Based Integration of DGPS and Vehicle Sensors for Localization
We present a position estimation scheme for cars based on the integration of global positioning system (GPS) with vehicle sensors. The aim is to achieve enough accuracy to enable in vehicle cooperative collision warning, i.e., systems that provides warnings to drivers based on information about the...
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| Veröffentlicht in: | IEEE transactions on control systems technology 2007-11, Vol.15 (6), p.1080-1088 |
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| container_title | IEEE transactions on control systems technology |
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| creator | Rezaei, S. Sengupta, R. |
| description | We present a position estimation scheme for cars based on the integration of global positioning system (GPS) with vehicle sensors. The aim is to achieve enough accuracy to enable in vehicle cooperative collision warning, i.e., systems that provides warnings to drivers based on information about the motions of neighboring vehicles obtained by wireless communications from those vehicles, without use of ranging sensors. The vehicle sensors consist of wheel speed sensors, steering angle encoder, and a fiber optic gyro. We fuse these in an extended Kalman filter. The process model is a dynamic bicycle model. We present data from about 60 km of driving in urban environments including stops, intersection turns, U-turns, and lane changes, at both low and high speeds. The data show the filter estimates position, speed, and heading with the accuracies required by cooperative collision warning in all except two kinds of settings. The data also shows GPS and vehicle sensor integration through a bicycle model compares favorably with position estimation by fusing GPS and inertial navigation system (INS) through a kinematic model. |
| doi_str_mv | 10.1109/TCST.2006.886439 |
| format | Article |
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The aim is to achieve enough accuracy to enable in vehicle cooperative collision warning, i.e., systems that provides warnings to drivers based on information about the motions of neighboring vehicles obtained by wireless communications from those vehicles, without use of ranging sensors. The vehicle sensors consist of wheel speed sensors, steering angle encoder, and a fiber optic gyro. We fuse these in an extended Kalman filter. The process model is a dynamic bicycle model. We present data from about 60 km of driving in urban environments including stops, intersection turns, U-turns, and lane changes, at both low and high speeds. The data show the filter estimates position, speed, and heading with the accuracies required by cooperative collision warning in all except two kinds of settings. 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The aim is to achieve enough accuracy to enable in vehicle cooperative collision warning, i.e., systems that provides warnings to drivers based on information about the motions of neighboring vehicles obtained by wireless communications from those vehicles, without use of ranging sensors. The vehicle sensors consist of wheel speed sensors, steering angle encoder, and a fiber optic gyro. We fuse these in an extended Kalman filter. The process model is a dynamic bicycle model. We present data from about 60 km of driving in urban environments including stops, intersection turns, U-turns, and lane changes, at both low and high speeds. The data show the filter estimates position, speed, and heading with the accuracies required by cooperative collision warning in all except two kinds of settings. The data also shows GPS and vehicle sensor integration through a bicycle model compares favorably with position estimation by fusing GPS and inertial navigation system (INS) through a kinematic model.</description><subject>Accuracy</subject><subject>Applied sciences</subject><subject>Automobiles</subject><subject>Automotive engineering</subject><subject>Bicycles</subject><subject>Computer science; control theory; systems</subject><subject>Control theory. Systems</subject><subject>Exact sciences and technology</subject><subject>Geographic information systems</subject><subject>Global Positioning System</subject><subject>Global Positioning System (GPS)</subject><subject>inertial navigation</subject><subject>Kalman filtering</subject><subject>Kalman filters</subject><subject>localization</subject><subject>navigation</subject><subject>Optical fiber sensors</subject><subject>Optical fibers</subject><subject>Optical sensors</subject><subject>safety</subject><subject>Satellite navigation systems</subject><subject>Sensor systems</subject><subject>Sensors</subject><subject>Vehicle driving</subject><subject>vehicles</subject><subject>Warning</subject><subject>Wheels</subject><subject>Wireless communication</subject><issn>1063-6536</issn><issn>1558-0865</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp90c9LwzAUB_AiCv68C16CoJ46X_qSNDnqdCoOFDa9hqxNtNI1mnQH_evNnCh48JRAPu8Led8s26cwoBTU6XQ4mQ4KADGQUjBUa9kW5VzmIAVfT3cQmAuOYjPbjvEFgDJelFvZ-Na0c9ORUdP2NuTnJtqa3HS9fQqmb3xHvCMXV_cTYrqaPNrnpmotmdgu-hCJ84GMfWXa5uML72YbzrTR7n2fO9nD6HI6vM7Hd1c3w7NxXjFF-1wwKypEaSqcGY61BKlASCpRAtaMmVICt1Sx2iEXyHldzqSsXWkcuhkg7mQnq9zX4N8WNvZ63sTKtq3prF9ELZWgSogCkjz-VyITQKEoEzz8A1_8InTpFzqtU6miUDwhWKEq-BiDdfo1NHMT3jUFvWxBL1vQyxb0qoU0cvSda2JalAumq5r4O6cKKFRJkztYucZa-_PMMGWUHD8Bha6NkQ</recordid><startdate>20071101</startdate><enddate>20071101</enddate><creator>Rezaei, S.</creator><creator>Sengupta, R.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Systems</topic><topic>Exact sciences and technology</topic><topic>Geographic information systems</topic><topic>Global Positioning System</topic><topic>Global Positioning System (GPS)</topic><topic>inertial navigation</topic><topic>Kalman filtering</topic><topic>Kalman filters</topic><topic>localization</topic><topic>navigation</topic><topic>Optical fiber sensors</topic><topic>Optical fibers</topic><topic>Optical sensors</topic><topic>safety</topic><topic>Satellite navigation systems</topic><topic>Sensor systems</topic><topic>Sensors</topic><topic>Vehicle driving</topic><topic>vehicles</topic><topic>Warning</topic><topic>Wheels</topic><topic>Wireless communication</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rezaei, S.</creatorcontrib><creatorcontrib>Sengupta, R.</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>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on control systems technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Rezaei, S.</au><au>Sengupta, R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Kalman Filter-Based Integration of DGPS and Vehicle Sensors for Localization</atitle><jtitle>IEEE transactions on control systems technology</jtitle><stitle>TCST</stitle><date>2007-11-01</date><risdate>2007</risdate><volume>15</volume><issue>6</issue><spage>1080</spage><epage>1088</epage><pages>1080-1088</pages><issn>1063-6536</issn><eissn>1558-0865</eissn><coden>IETTE2</coden><abstract>We present a position estimation scheme for cars based on the integration of global positioning system (GPS) with vehicle sensors. The aim is to achieve enough accuracy to enable in vehicle cooperative collision warning, i.e., systems that provides warnings to drivers based on information about the motions of neighboring vehicles obtained by wireless communications from those vehicles, without use of ranging sensors. The vehicle sensors consist of wheel speed sensors, steering angle encoder, and a fiber optic gyro. We fuse these in an extended Kalman filter. The process model is a dynamic bicycle model. We present data from about 60 km of driving in urban environments including stops, intersection turns, U-turns, and lane changes, at both low and high speeds. The data show the filter estimates position, speed, and heading with the accuracies required by cooperative collision warning in all except two kinds of settings. 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| source | IEEE Electronic Library (IEL) |
| subjects | Accuracy Applied sciences Automobiles Automotive engineering Bicycles Computer science control theory systems Control theory. Systems Exact sciences and technology Geographic information systems Global Positioning System Global Positioning System (GPS) inertial navigation Kalman filtering Kalman filters localization navigation Optical fiber sensors Optical fibers Optical sensors safety Satellite navigation systems Sensor systems Sensors Vehicle driving vehicles Warning Wheels Wireless communication |
| title | Kalman Filter-Based Integration of DGPS and Vehicle Sensors for Localization |
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