Adaptive Robust Minimum Error Entropy Unscented Kalman Filter for Satellite Attitude Estimation
AbstractIn recent years, the Kalman filter based on the minimum error entropy (MEE) criterion has been proposed, which outperforms the traditional Kalman filter in the presence of non-Gaussian noise. In practical applications, the estimated performance of the MEE unscented Kalman filter (MEE-UKF) al...
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Veröffentlicht in: | Journal of aerospace engineering 2022-09, Vol.35 (5) |
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creator | Qian, Huaming Chu, Shuai Zhao, Di |
description | AbstractIn recent years, the Kalman filter based on the minimum error entropy (MEE) criterion has been proposed, which outperforms the traditional Kalman filter in the presence of non-Gaussian noise. In practical applications, the estimated performance of the MEE unscented Kalman filter (MEE-UKF) algorithm is influenced by the kernel bandwidth (KB). In addition, it may be unstable in numerical computation. This paper proposes an adaptive robust MEE unscented Kalman filter (AMEE-UKF) to address the problem of instability in numerical computation. In addition, by setting an adaptive factor to optimize the MEE-UKF, an appropriate value of the KB can be obtained adaptively. The high accuracy and robustness of the AMEE-UKF were demonstrated by the simulation experiments. |
doi_str_mv | 10.1061/(ASCE)AS.1943-5525.0001456 |
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In practical applications, the estimated performance of the MEE unscented Kalman filter (MEE-UKF) algorithm is influenced by the kernel bandwidth (KB). In addition, it may be unstable in numerical computation. This paper proposes an adaptive robust MEE unscented Kalman filter (AMEE-UKF) to address the problem of instability in numerical computation. In addition, by setting an adaptive factor to optimize the MEE-UKF, an appropriate value of the KB can be obtained adaptively. The high accuracy and robustness of the AMEE-UKF were demonstrated by the simulation experiments.</description><identifier>ISSN: 0893-1321</identifier><identifier>EISSN: 1943-5525</identifier><identifier>DOI: 10.1061/(ASCE)AS.1943-5525.0001456</identifier><language>eng</language><publisher>New York: American Society of Civil Engineers</publisher><subject>Algorithms ; Computation ; Entropy ; Kalman filters ; Numerical analysis ; Random noise ; Robustness (mathematics) ; Technical Papers</subject><ispartof>Journal of aerospace engineering, 2022-09, Vol.35 (5)</ispartof><rights>2022 American Society of Civil Engineers</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a337t-5212ee31a629bf95f9fde97559498e329ff90091004c90b1d066f537cba9a63</citedby><cites>FETCH-LOGICAL-a337t-5212ee31a629bf95f9fde97559498e329ff90091004c90b1d066f537cba9a63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttp://ascelibrary.org/doi/pdf/10.1061/(ASCE)AS.1943-5525.0001456$$EPDF$$P50$$Gasce$$H</linktopdf><linktohtml>$$Uhttp://ascelibrary.org/doi/abs/10.1061/(ASCE)AS.1943-5525.0001456$$EHTML$$P50$$Gasce$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,76193,76201</link.rule.ids></links><search><creatorcontrib>Qian, Huaming</creatorcontrib><creatorcontrib>Chu, Shuai</creatorcontrib><creatorcontrib>Zhao, Di</creatorcontrib><title>Adaptive Robust Minimum Error Entropy Unscented Kalman Filter for Satellite Attitude Estimation</title><title>Journal of aerospace engineering</title><description>AbstractIn recent years, the Kalman filter based on the minimum error entropy (MEE) criterion has been proposed, which outperforms the traditional Kalman filter in the presence of non-Gaussian noise. In practical applications, the estimated performance of the MEE unscented Kalman filter (MEE-UKF) algorithm is influenced by the kernel bandwidth (KB). In addition, it may be unstable in numerical computation. This paper proposes an adaptive robust MEE unscented Kalman filter (AMEE-UKF) to address the problem of instability in numerical computation. In addition, by setting an adaptive factor to optimize the MEE-UKF, an appropriate value of the KB can be obtained adaptively. The high accuracy and robustness of the AMEE-UKF were demonstrated by the simulation experiments.</description><subject>Algorithms</subject><subject>Computation</subject><subject>Entropy</subject><subject>Kalman filters</subject><subject>Numerical analysis</subject><subject>Random noise</subject><subject>Robustness (mathematics)</subject><subject>Technical Papers</subject><issn>0893-1321</issn><issn>1943-5525</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp1kE1LxDAQhoMouK7-h6AXPXTNR5NuvJWlq-KKYPUc0jaBLP0ySRf239uyq568zMDwvDPDA8A1RguMOL6_TfNVdpfmCyxiGjFG2AIhhGPGT8Dsd3YKZmgpaIQpwefgwvvtxHBBZkCmleqD3Wn43hWDD_DVtrYZGpg51zmYtcF1_R5-tr7UbdAVfFF1o1q4tnXQDpqRyVXQdW2DhmkINgyVhpkPtlHBdu0lODOq9vrq2OcgX2cfq6do8_b4vEo3kaI0CREjmGhNseJEFEYwI0ylRcKYiMVSUyKMEQgJjFBcClTgCnFuGE3KQgnF6RzcHLb2rvsatA9y2w2uHQ9KwrlIOKGYjNTDgSpd573TRvZufNPtJUZy0inlpHMsclInJ3XyqHMM80NYjSb-1v8k_w9-A_PXeY8</recordid><startdate>20220901</startdate><enddate>20220901</enddate><creator>Qian, Huaming</creator><creator>Chu, Shuai</creator><creator>Zhao, Di</creator><general>American Society of Civil Engineers</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>KR7</scope><scope>L7M</scope></search><sort><creationdate>20220901</creationdate><title>Adaptive Robust Minimum Error Entropy Unscented Kalman Filter for Satellite Attitude Estimation</title><author>Qian, Huaming ; Chu, Shuai ; Zhao, Di</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a337t-5212ee31a629bf95f9fde97559498e329ff90091004c90b1d066f537cba9a63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Computation</topic><topic>Entropy</topic><topic>Kalman filters</topic><topic>Numerical analysis</topic><topic>Random noise</topic><topic>Robustness (mathematics)</topic><topic>Technical Papers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Qian, Huaming</creatorcontrib><creatorcontrib>Chu, Shuai</creatorcontrib><creatorcontrib>Zhao, Di</creatorcontrib><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Journal of aerospace engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Qian, Huaming</au><au>Chu, Shuai</au><au>Zhao, Di</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive Robust Minimum Error Entropy Unscented Kalman Filter for Satellite Attitude Estimation</atitle><jtitle>Journal of aerospace engineering</jtitle><date>2022-09-01</date><risdate>2022</risdate><volume>35</volume><issue>5</issue><issn>0893-1321</issn><eissn>1943-5525</eissn><abstract>AbstractIn recent years, the Kalman filter based on the minimum error entropy (MEE) criterion has been proposed, which outperforms the traditional Kalman filter in the presence of non-Gaussian noise. In practical applications, the estimated performance of the MEE unscented Kalman filter (MEE-UKF) algorithm is influenced by the kernel bandwidth (KB). In addition, it may be unstable in numerical computation. This paper proposes an adaptive robust MEE unscented Kalman filter (AMEE-UKF) to address the problem of instability in numerical computation. In addition, by setting an adaptive factor to optimize the MEE-UKF, an appropriate value of the KB can be obtained adaptively. The high accuracy and robustness of the AMEE-UKF were demonstrated by the simulation experiments.</abstract><cop>New York</cop><pub>American Society of Civil Engineers</pub><doi>10.1061/(ASCE)AS.1943-5525.0001456</doi></addata></record> |
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subjects | Algorithms Computation Entropy Kalman filters Numerical analysis Random noise Robustness (mathematics) Technical Papers |
title | Adaptive Robust Minimum Error Entropy Unscented Kalman Filter for Satellite Attitude Estimation |
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