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Acceleration Characterization for Reentry Orbit Determination with Unmodeled Maneuvers
A new acceleration characterization filter is formulated for reentry orbit determination with unmodeled maneuvers. Drag and lift accelerations are treated as deterministic disturbances that can be characterized statistically and included in covariance predictions. Mean-square acceleration covariance...
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Published in: | Journal of guidance, control, and dynamics control, and dynamics, 2018-07, Vol.41 (7), p.1463-1475 |
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container_title | Journal of guidance, control, and dynamics |
container_volume | 41 |
creator | Hough, Michael E |
description | A new acceleration characterization filter is formulated for reentry orbit determination with unmodeled maneuvers. Drag and lift accelerations are treated as deterministic disturbances that can be characterized statistically and included in covariance predictions. Mean-square acceleration covariances are determined by a statistical analysis of expected maneuvers. Adaptation to the dynamic reentry environment is improved because the mean-square acceleration covariance depends on dynamic pressure. Performance simulations demonstrate the accuracy and effectiveness of this adaptive filter for demanding reentry maneuvers. Monte Carlo techniques assess accuracy sensitivity to modeling assumptions and to offnominal trajectories. |
doi_str_mv | 10.2514/1.G003359 |
format | article |
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Drag and lift accelerations are treated as deterministic disturbances that can be characterized statistically and included in covariance predictions. Mean-square acceleration covariances are determined by a statistical analysis of expected maneuvers. Adaptation to the dynamic reentry environment is improved because the mean-square acceleration covariance depends on dynamic pressure. Performance simulations demonstrate the accuracy and effectiveness of this adaptive filter for demanding reentry maneuvers. Monte Carlo techniques assess accuracy sensitivity to modeling assumptions and to offnominal trajectories.</description><identifier>ISSN: 0731-5090</identifier><identifier>EISSN: 1533-3884</identifier><identifier>DOI: 10.2514/1.G003359</identifier><language>eng</language><publisher>Reston: American Institute of Aeronautics and Astronautics</publisher><subject>Acceleration ; Accuracy ; Adaptation ; Adaptive filters ; Algorithms ; Altitude ; Bias ; Computer simulation ; Covariance ; Dynamic pressure ; Kalman filters ; Model accuracy ; Noise ; Orbit determination ; Orbital maneuvers ; Reentry ; Sensitivity analysis ; Statistical analysis ; Trajectory analysis ; Velocity</subject><ispartof>Journal of guidance, control, and dynamics, 2018-07, Vol.41 (7), p.1463-1475</ispartof><rights>Copyright © 2018 by Michael E. Hough. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. All requests for copying and permission to reprint should be submitted to CCC at www.copyright.com; employ the ISSN 0731-5090 (print) or 1533-3884 (online) to initiate your request. See also AIAA Rights and Permissions www.aiaa.org/randp.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c313t-886bae4baaa4f1939141c1249386d45e389217c77119e7f0f6ca53c80cb81ed43</citedby><cites>FETCH-LOGICAL-c313t-886bae4baaa4f1939141c1249386d45e389217c77119e7f0f6ca53c80cb81ed43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Hough, Michael E</creatorcontrib><title>Acceleration Characterization for Reentry Orbit Determination with Unmodeled Maneuvers</title><title>Journal of guidance, control, and dynamics</title><description>A new acceleration characterization filter is formulated for reentry orbit determination with unmodeled maneuvers. Drag and lift accelerations are treated as deterministic disturbances that can be characterized statistically and included in covariance predictions. Mean-square acceleration covariances are determined by a statistical analysis of expected maneuvers. Adaptation to the dynamic reentry environment is improved because the mean-square acceleration covariance depends on dynamic pressure. Performance simulations demonstrate the accuracy and effectiveness of this adaptive filter for demanding reentry maneuvers. Monte Carlo techniques assess accuracy sensitivity to modeling assumptions and to offnominal trajectories.</description><subject>Acceleration</subject><subject>Accuracy</subject><subject>Adaptation</subject><subject>Adaptive filters</subject><subject>Algorithms</subject><subject>Altitude</subject><subject>Bias</subject><subject>Computer simulation</subject><subject>Covariance</subject><subject>Dynamic pressure</subject><subject>Kalman filters</subject><subject>Model accuracy</subject><subject>Noise</subject><subject>Orbit determination</subject><subject>Orbital maneuvers</subject><subject>Reentry</subject><subject>Sensitivity analysis</subject><subject>Statistical analysis</subject><subject>Trajectory analysis</subject><subject>Velocity</subject><issn>0731-5090</issn><issn>1533-3884</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp9kU9Lw0AQxRdRMFYPfoOAJw-pO5n9eyxVq1ApiPUaNpsJTWmTukmV-umNpOcyhwfzfrx3eIzdAh-nEsQDjGecI0p7xiKQiAkaI85ZxDVCIrnll-yqbdecAyrQEfuceE8bCq6rmjqerlxwvqNQ_Q6PsgnxO1HdhUO8CHnVxY_U29uqHvyfqlvFy3rbFH1IEb-5mvbfFNprdlG6TUs3Rx2x5fPTx_QlmS9mr9PJPPEI2CXGqNyRyJ1zogSLFgR4SIVFowohCY1NQXutASzpkpfKO4necJ8boELgiN0NubvQfO2p7bJ1sw91X5n1KcLw_tRJikslpNR9-UmKW6VVakxP3Q-UD03bBiqzXai2Lhwy4Nn_BBlkxwnwD-tYdsA</recordid><startdate>20180701</startdate><enddate>20180701</enddate><creator>Hough, Michael E</creator><general>American Institute of Aeronautics and Astronautics</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20180701</creationdate><title>Acceleration Characterization for Reentry Orbit Determination with Unmodeled Maneuvers</title><author>Hough, Michael E</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c313t-886bae4baaa4f1939141c1249386d45e389217c77119e7f0f6ca53c80cb81ed43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Acceleration</topic><topic>Accuracy</topic><topic>Adaptation</topic><topic>Adaptive filters</topic><topic>Algorithms</topic><topic>Altitude</topic><topic>Bias</topic><topic>Computer simulation</topic><topic>Covariance</topic><topic>Dynamic pressure</topic><topic>Kalman filters</topic><topic>Model accuracy</topic><topic>Noise</topic><topic>Orbit determination</topic><topic>Orbital maneuvers</topic><topic>Reentry</topic><topic>Sensitivity analysis</topic><topic>Statistical analysis</topic><topic>Trajectory analysis</topic><topic>Velocity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hough, Michael E</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>Aerospace 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>Journal of guidance, control, and dynamics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hough, Michael E</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Acceleration Characterization for Reentry Orbit Determination with Unmodeled Maneuvers</atitle><jtitle>Journal of guidance, control, and dynamics</jtitle><date>2018-07-01</date><risdate>2018</risdate><volume>41</volume><issue>7</issue><spage>1463</spage><epage>1475</epage><pages>1463-1475</pages><issn>0731-5090</issn><eissn>1533-3884</eissn><abstract>A new acceleration characterization filter is formulated for reentry orbit determination with unmodeled maneuvers. Drag and lift accelerations are treated as deterministic disturbances that can be characterized statistically and included in covariance predictions. Mean-square acceleration covariances are determined by a statistical analysis of expected maneuvers. Adaptation to the dynamic reentry environment is improved because the mean-square acceleration covariance depends on dynamic pressure. Performance simulations demonstrate the accuracy and effectiveness of this adaptive filter for demanding reentry maneuvers. Monte Carlo techniques assess accuracy sensitivity to modeling assumptions and to offnominal trajectories.</abstract><cop>Reston</cop><pub>American Institute of Aeronautics and Astronautics</pub><doi>10.2514/1.G003359</doi><tpages>13</tpages></addata></record> |
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subjects | Acceleration Accuracy Adaptation Adaptive filters Algorithms Altitude Bias Computer simulation Covariance Dynamic pressure Kalman filters Model accuracy Noise Orbit determination Orbital maneuvers Reentry Sensitivity analysis Statistical analysis Trajectory analysis Velocity |
title | Acceleration Characterization for Reentry Orbit Determination with Unmodeled Maneuvers |
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