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Random Weighting Estimation Method for Dynamic Navigation Positioning
This paper presents a new random weighting estimation method for dynamic navigation positioning. This method adopts the concept of random weighting estimation to estimate the covariance matrices of system state noises and observation noises for controlling the disturbances of singular observations a...
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Published in: | Chinese journal of aeronautics 2011-06, Vol.24 (3), p.318-323 |
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creator | GAO, Shesheng GAO, Yi ZHONG, Yongmin WEI, Wenhui |
description | This paper presents a new random weighting estimation method for dynamic navigation positioning. This method adopts the concept of random weighting estimation to estimate the covariance matrices of system state noises and observation noises for controlling the disturbances of singular observations and the kinematic model errors. It satisfies the practical requirements of the residual vector and innovation vector to sufficiently utilize observation information, thus weakening the disturbing effect of the kinematic model error and observation model error on the state parameter estimation. Theories and algorithms of random weighting estimation are established for estimating the covariance matrices of observation residual vectors and innovation vec- tors. This random weighting estimation method provides an effective solution for improving the positioning accuracy in dynamic navigation. Experimental results show that compared with the Kalman filtering, the extended Kalman filtering and the adaptive windowing filtering, the proposed method can adaptively determine the covariance matrices of observation error and state error, effectively resist the disturbances caused by system error and observation error, and significantly improve the positioning accu- racy for dynamic navigation. |
doi_str_mv | 10.1016/S1000-9361(11)60037-X |
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This method adopts the concept of random weighting estimation to estimate the covariance matrices of system state noises and observation noises for controlling the disturbances of singular observations and the kinematic model errors. It satisfies the practical requirements of the residual vector and innovation vector to sufficiently utilize observation information, thus weakening the disturbing effect of the kinematic model error and observation model error on the state parameter estimation. Theories and algorithms of random weighting estimation are established for estimating the covariance matrices of observation residual vectors and innovation vec- tors. This random weighting estimation method provides an effective solution for improving the positioning accuracy in dynamic navigation. Experimental results show that compared with the Kalman filtering, the extended Kalman filtering and the adaptive windowing filtering, the proposed method can adaptively determine the covariance matrices of observation error and state error, effectively resist the disturbances caused by system error and observation error, and significantly improve the positioning accu- racy for dynamic navigation.</description><identifier>ISSN: 1000-9361</identifier><identifier>DOI: 10.1016/S1000-9361(11)60037-X</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>covariance matrix ; dynamic navigation positioning ; Dynamical systems ; Dynamics ; error ; Errors ; estimation ; kinematic model error ; Mathematical analysis ; Mathematical models ; Navigation ; observation model error ; random weighting estimation ; Vectors (mathematics) ; Weighting ; 估计方法 ; 协方差矩阵 ; 导航定位精度 ; 扩展卡尔曼滤波 ; 自适应窗口 ; 误差控制 ; 运动学模型 ; 随机加权估计</subject><ispartof>Chinese journal of aeronautics, 2011-06, Vol.24 (3), p.318-323</ispartof><rights>2011 Chinese Journal of Aeronautics</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c367t-ff4089549f0863e729c0ba58f9c5a2e4c4fd992a8d9be626f64e20afe9513df23</citedby><cites>FETCH-LOGICAL-c367t-ff4089549f0863e729c0ba58f9c5a2e4c4fd992a8d9be626f64e20afe9513df23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/83889X/83889X.jpg</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S100093611160037X$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,3549,27924,27925,45780</link.rule.ids></links><search><creatorcontrib>GAO, Shesheng</creatorcontrib><creatorcontrib>GAO, Yi</creatorcontrib><creatorcontrib>ZHONG, Yongmin</creatorcontrib><creatorcontrib>WEI, Wenhui</creatorcontrib><title>Random Weighting Estimation Method for Dynamic Navigation Positioning</title><title>Chinese journal of aeronautics</title><addtitle>Chinese Journal of Aeronautics</addtitle><description>This paper presents a new random weighting estimation method for dynamic navigation positioning. This method adopts the concept of random weighting estimation to estimate the covariance matrices of system state noises and observation noises for controlling the disturbances of singular observations and the kinematic model errors. It satisfies the practical requirements of the residual vector and innovation vector to sufficiently utilize observation information, thus weakening the disturbing effect of the kinematic model error and observation model error on the state parameter estimation. Theories and algorithms of random weighting estimation are established for estimating the covariance matrices of observation residual vectors and innovation vec- tors. This random weighting estimation method provides an effective solution for improving the positioning accuracy in dynamic navigation. Experimental results show that compared with the Kalman filtering, the extended Kalman filtering and the adaptive windowing filtering, the proposed method can adaptively determine the covariance matrices of observation error and state error, effectively resist the disturbances caused by system error and observation error, and significantly improve the positioning accu- racy for dynamic navigation.</description><subject>covariance matrix</subject><subject>dynamic navigation positioning</subject><subject>Dynamical systems</subject><subject>Dynamics</subject><subject>error</subject><subject>Errors</subject><subject>estimation</subject><subject>kinematic model error</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Navigation</subject><subject>observation model error</subject><subject>random weighting estimation</subject><subject>Vectors (mathematics)</subject><subject>Weighting</subject><subject>估计方法</subject><subject>协方差矩阵</subject><subject>导航定位精度</subject><subject>扩展卡尔曼滤波</subject><subject>自适应窗口</subject><subject>误差控制</subject><subject>运动学模型</subject><subject>随机加权估计</subject><issn>1000-9361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNqFkMtOwzAQRb0AiVL4BKSwAhYBO3aceIVQKQ-pPMRDdGe5zjg1auLWTiv170mbqltWM5qZe6_mIHRG8DXBhN98EoxxLCgnl4RccYxpFo8PUG8_PkLHIfy2c5ER3EPDD1UXrop-wJbTxtZlNAyNrVRjXR29QDN1RWScj-7Xtaqsjl7Vypbd9t0Fu2la0Qk6NGoW4HRX--j7Yfg1eIpHb4_Pg7tRrCnPmtgYhnORMmFwzilkidB4otLcCJ2qBJhmphAiUXkhJsATbjiDBCsDIiW0MAnto4vOd-7dYgmhkZUNGmYzVYNbBilaBCLJGW8v0-5SexeCByPnvn3LryXBckNKbknJDRJJiNySkuNWd9vpoH1jZcHLoC3UGgrrQTeycPZfh_Nd8tTV5aKls4-mOWMZyxP6B0bkffU</recordid><startdate>20110601</startdate><enddate>20110601</enddate><creator>GAO, Shesheng</creator><creator>GAO, Yi</creator><creator>ZHONG, Yongmin</creator><creator>WEI, Wenhui</creator><general>Elsevier Ltd</general><scope>2RA</scope><scope>92L</scope><scope>CQIGP</scope><scope>W92</scope><scope>~WA</scope><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20110601</creationdate><title>Random Weighting Estimation Method for Dynamic Navigation Positioning</title><author>GAO, Shesheng ; GAO, Yi ; ZHONG, Yongmin ; WEI, Wenhui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c367t-ff4089549f0863e729c0ba58f9c5a2e4c4fd992a8d9be626f64e20afe9513df23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>covariance matrix</topic><topic>dynamic navigation positioning</topic><topic>Dynamical systems</topic><topic>Dynamics</topic><topic>error</topic><topic>Errors</topic><topic>estimation</topic><topic>kinematic model error</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Navigation</topic><topic>observation model error</topic><topic>random weighting estimation</topic><topic>Vectors (mathematics)</topic><topic>Weighting</topic><topic>估计方法</topic><topic>协方差矩阵</topic><topic>导航定位精度</topic><topic>扩展卡尔曼滤波</topic><topic>自适应窗口</topic><topic>误差控制</topic><topic>运动学模型</topic><topic>随机加权估计</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>GAO, Shesheng</creatorcontrib><creatorcontrib>GAO, Yi</creatorcontrib><creatorcontrib>ZHONG, Yongmin</creatorcontrib><creatorcontrib>WEI, Wenhui</creatorcontrib><collection>维普_期刊</collection><collection>中文科技期刊数据库-CALIS站点</collection><collection>维普中文期刊数据库</collection><collection>中文科技期刊数据库-工程技术</collection><collection>中文科技期刊数据库- 镜像站点</collection><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Chinese journal of aeronautics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>GAO, Shesheng</au><au>GAO, Yi</au><au>ZHONG, Yongmin</au><au>WEI, Wenhui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Random Weighting Estimation Method for Dynamic Navigation Positioning</atitle><jtitle>Chinese journal of aeronautics</jtitle><addtitle>Chinese Journal of Aeronautics</addtitle><date>2011-06-01</date><risdate>2011</risdate><volume>24</volume><issue>3</issue><spage>318</spage><epage>323</epage><pages>318-323</pages><issn>1000-9361</issn><abstract>This paper presents a new random weighting estimation method for dynamic navigation positioning. This method adopts the concept of random weighting estimation to estimate the covariance matrices of system state noises and observation noises for controlling the disturbances of singular observations and the kinematic model errors. It satisfies the practical requirements of the residual vector and innovation vector to sufficiently utilize observation information, thus weakening the disturbing effect of the kinematic model error and observation model error on the state parameter estimation. Theories and algorithms of random weighting estimation are established for estimating the covariance matrices of observation residual vectors and innovation vec- tors. This random weighting estimation method provides an effective solution for improving the positioning accuracy in dynamic navigation. Experimental results show that compared with the Kalman filtering, the extended Kalman filtering and the adaptive windowing filtering, the proposed method can adaptively determine the covariance matrices of observation error and state error, effectively resist the disturbances caused by system error and observation error, and significantly improve the positioning accu- racy for dynamic navigation.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/S1000-9361(11)60037-X</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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subjects | covariance matrix dynamic navigation positioning Dynamical systems Dynamics error Errors estimation kinematic model error Mathematical analysis Mathematical models Navigation observation model error random weighting estimation Vectors (mathematics) Weighting 估计方法 协方差矩阵 导航定位精度 扩展卡尔曼滤波 自适应窗口 误差控制 运动学模型 随机加权估计 |
title | Random Weighting Estimation Method for Dynamic Navigation Positioning |
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