Pseudorange error prediction for adaptive tightly coupled GNSS/IMU navigation in urban areas
The integration of global navigation satellite systems (GNSS) and inertial measurement unit (IMU) with the Kalman filter is widely used to enhance the availability of positioning in urban areas for many intelligent transport system (ITS) applications. In the traditional Kalman filter, the GNSS measu...
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| Veröffentlicht in: | GPS solutions 2022, Vol.26 (1), Article 28 |
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| creator | Sun, Rui Zhang, Zixuan Cheng, Qi Ochieng, Washington Yotto |
| description | The integration of global navigation satellite systems (GNSS) and inertial measurement unit (IMU) with the Kalman filter is widely used to enhance the availability of positioning in urban areas for many intelligent transport system (ITS) applications. In the traditional Kalman filter, the GNSS measurement noise is fixed based on factors determined a priori, instead of reflecting the impact of the surrounding environment on the received GNSS signal. This has the effect of degrading position accuracy and the a posteriori quality indicators. To address this issue, we propose a new measurement noise covariance update scheme, with the adaptive indicator generated from pseudorange error prediction results, for a tightly coupled GNSS/IMU navigation system in urban areas. Specifically, the pseudorange errors are predicted by means of an ensemble bagged regression tree model accounting for signal strength, satellite elevation angle and coordinate information. The urban experimental results show that the proposed algorithm provides a 3D accuracy of 9.21 m, with an improvement of 55% and 15%, respectively, over the traditional fixed covariance extended Kalman filter (EKF)-based fusion and EKF-based fusion with pseudorange error correction. |
| doi_str_mv | 10.1007/s10291-021-01213-z |
| format | Article |
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In the traditional Kalman filter, the GNSS measurement noise is fixed based on factors determined a priori, instead of reflecting the impact of the surrounding environment on the received GNSS signal. This has the effect of degrading position accuracy and the a posteriori quality indicators. To address this issue, we propose a new measurement noise covariance update scheme, with the adaptive indicator generated from pseudorange error prediction results, for a tightly coupled GNSS/IMU navigation system in urban areas. Specifically, the pseudorange errors are predicted by means of an ensemble bagged regression tree model accounting for signal strength, satellite elevation angle and coordinate information. The urban experimental results show that the proposed algorithm provides a 3D accuracy of 9.21 m, with an improvement of 55% and 15%, respectively, over the traditional fixed covariance extended Kalman filter (EKF)-based fusion and EKF-based fusion with pseudorange error correction.</description><identifier>ISSN: 1080-5370</identifier><identifier>EISSN: 1521-1886</identifier><identifier>DOI: 10.1007/s10291-021-01213-z</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Atmospheric Sciences ; Automotive Engineering ; Covariance ; Earth and Environmental Science ; Earth Sciences ; Electrical Engineering ; Elevation angle ; Error correction ; Extended Kalman filter ; Geophysics/Geodesy ; Global navigation satellite system ; GNSS Integrity ; Inertial platforms ; Intelligent transportation systems ; Navigation satellites ; Navigation systems ; Noise measurement ; Original Article ; Position indicators ; Regression analysis ; Regression models ; Signal strength ; Space Exploration and Astronautics ; Space Sciences (including Extraterrestrial Physics ; Urban areas</subject><ispartof>GPS solutions, 2022, Vol.26 (1), Article 28</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-f9b421a5de3b6019a7aad8328bf97546cc93fcf97a8c8a311eb9cbe4ff1fe9a13</citedby><cites>FETCH-LOGICAL-c319t-f9b421a5de3b6019a7aad8328bf97546cc93fcf97a8c8a311eb9cbe4ff1fe9a13</cites><orcidid>0000-0003-2252-9944</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10291-021-01213-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10291-021-01213-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Sun, Rui</creatorcontrib><creatorcontrib>Zhang, Zixuan</creatorcontrib><creatorcontrib>Cheng, Qi</creatorcontrib><creatorcontrib>Ochieng, Washington Yotto</creatorcontrib><title>Pseudorange error prediction for adaptive tightly coupled GNSS/IMU navigation in urban areas</title><title>GPS solutions</title><addtitle>GPS Solut</addtitle><description>The integration of global navigation satellite systems (GNSS) and inertial measurement unit (IMU) with the Kalman filter is widely used to enhance the availability of positioning in urban areas for many intelligent transport system (ITS) applications. In the traditional Kalman filter, the GNSS measurement noise is fixed based on factors determined a priori, instead of reflecting the impact of the surrounding environment on the received GNSS signal. This has the effect of degrading position accuracy and the a posteriori quality indicators. To address this issue, we propose a new measurement noise covariance update scheme, with the adaptive indicator generated from pseudorange error prediction results, for a tightly coupled GNSS/IMU navigation system in urban areas. Specifically, the pseudorange errors are predicted by means of an ensemble bagged regression tree model accounting for signal strength, satellite elevation angle and coordinate information. The urban experimental results show that the proposed algorithm provides a 3D accuracy of 9.21 m, with an improvement of 55% and 15%, respectively, over the traditional fixed covariance extended Kalman filter (EKF)-based fusion and EKF-based fusion with pseudorange error correction.</description><subject>Algorithms</subject><subject>Atmospheric Sciences</subject><subject>Automotive Engineering</subject><subject>Covariance</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Electrical Engineering</subject><subject>Elevation angle</subject><subject>Error correction</subject><subject>Extended Kalman filter</subject><subject>Geophysics/Geodesy</subject><subject>Global navigation satellite system</subject><subject>GNSS Integrity</subject><subject>Inertial platforms</subject><subject>Intelligent transportation systems</subject><subject>Navigation satellites</subject><subject>Navigation systems</subject><subject>Noise measurement</subject><subject>Original Article</subject><subject>Position indicators</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Signal strength</subject><subject>Space Exploration and Astronautics</subject><subject>Space Sciences (including Extraterrestrial Physics</subject><subject>Urban areas</subject><issn>1080-5370</issn><issn>1521-1886</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kM1LAzEQxRdRsFb_AU8Bz2szyX4kRylaC_UDam9CmM0mdUvdXZPdQvvXG7uCNw_DzIP33sAviq6B3gKl-cQDZRJiysIAAx4fTqIRpEGCENlpuKmgccpzeh5deL-hlFEpk1H0_upNXzYO67UhxrnGkdaZstJd1dTEBokltl21M6Sr1h_ddk9007dbU5LZ83I5mT-tSI27ao3HQFWT3hVYE3QG_WV0ZnHrzdXvHkerh_u36WO8eJnNp3eLWHOQXWxlkTDAtDS8yChIzBFLwZkorMzTJNNacqvDjUIL5ACmkLowibVgjUTg4-hm6G1d89Ub36lN07s6vFQsgwBIiCQJLja4tGu8d8aq1lWf6PYKqPqhqAaKKlBUR4rqEEJ8CPlgDozcX_U_qW-Cenb4</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Sun, Rui</creator><creator>Zhang, Zixuan</creator><creator>Cheng, Qi</creator><creator>Ochieng, Washington Yotto</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>H8D</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0003-2252-9944</orcidid></search><sort><creationdate>2022</creationdate><title>Pseudorange error prediction for adaptive tightly coupled GNSS/IMU navigation in urban areas</title><author>Sun, Rui ; Zhang, Zixuan ; Cheng, Qi ; Ochieng, Washington Yotto</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-f9b421a5de3b6019a7aad8328bf97546cc93fcf97a8c8a311eb9cbe4ff1fe9a13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Atmospheric Sciences</topic><topic>Automotive Engineering</topic><topic>Covariance</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Electrical Engineering</topic><topic>Elevation angle</topic><topic>Error correction</topic><topic>Extended Kalman filter</topic><topic>Geophysics/Geodesy</topic><topic>Global navigation satellite system</topic><topic>GNSS Integrity</topic><topic>Inertial platforms</topic><topic>Intelligent transportation systems</topic><topic>Navigation satellites</topic><topic>Navigation systems</topic><topic>Noise measurement</topic><topic>Original Article</topic><topic>Position indicators</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Signal strength</topic><topic>Space Exploration and Astronautics</topic><topic>Space Sciences (including Extraterrestrial Physics</topic><topic>Urban areas</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sun, Rui</creatorcontrib><creatorcontrib>Zhang, Zixuan</creatorcontrib><creatorcontrib>Cheng, Qi</creatorcontrib><creatorcontrib>Ochieng, Washington Yotto</creatorcontrib><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Aerospace Database</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>GPS solutions</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sun, Rui</au><au>Zhang, Zixuan</au><au>Cheng, Qi</au><au>Ochieng, Washington Yotto</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Pseudorange error prediction for adaptive tightly coupled GNSS/IMU navigation in urban areas</atitle><jtitle>GPS solutions</jtitle><stitle>GPS Solut</stitle><date>2022</date><risdate>2022</risdate><volume>26</volume><issue>1</issue><artnum>28</artnum><issn>1080-5370</issn><eissn>1521-1886</eissn><abstract>The integration of global navigation satellite systems (GNSS) and inertial measurement unit (IMU) with the Kalman filter is widely used to enhance the availability of positioning in urban areas for many intelligent transport system (ITS) applications. In the traditional Kalman filter, the GNSS measurement noise is fixed based on factors determined a priori, instead of reflecting the impact of the surrounding environment on the received GNSS signal. This has the effect of degrading position accuracy and the a posteriori quality indicators. To address this issue, we propose a new measurement noise covariance update scheme, with the adaptive indicator generated from pseudorange error prediction results, for a tightly coupled GNSS/IMU navigation system in urban areas. Specifically, the pseudorange errors are predicted by means of an ensemble bagged regression tree model accounting for signal strength, satellite elevation angle and coordinate information. 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| subjects | Algorithms Atmospheric Sciences Automotive Engineering Covariance Earth and Environmental Science Earth Sciences Electrical Engineering Elevation angle Error correction Extended Kalman filter Geophysics/Geodesy Global navigation satellite system GNSS Integrity Inertial platforms Intelligent transportation systems Navigation satellites Navigation systems Noise measurement Original Article Position indicators Regression analysis Regression models Signal strength Space Exploration and Astronautics Space Sciences (including Extraterrestrial Physics Urban areas |
| title | Pseudorange error prediction for adaptive tightly coupled GNSS/IMU navigation in urban areas |
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