An Adaptive Estimator for Time Varying Processes with Maneuvers
An adaptive discrete Kalman filter is developed which is based on an improved covariance matching technique. The filter quickly finds the unknown process noise covariance implied by the observed data. The process noise covariance is assumed constant. all other filter parameters are assumed known, al...
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| creator | Schiefer,Michael A |
| description | An adaptive discrete Kalman filter is developed which is based on an improved covariance matching technique. The filter quickly finds the unknown process noise covariance implied by the observed data. The process noise covariance is assumed constant. all other filter parameters are assumed known, although possibly time varying. This filter, used in conjunction with a multiple model filter, can be used to iteratively estimate noise statistics and detect maneuvers. (Author) |
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| fullrecord | <record><control><sourceid>dtic_1RU</sourceid><recordid>TN_cdi_dtic_stinet_ADA172837</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>ADA172837</sourcerecordid><originalsourceid>FETCH-dtic_stinet_ADA1728373</originalsourceid><addsrcrecordid>eNrjZLB3zFNwTEksKMksS1VwLS7JzE0syS9SSAPikMzcVIWwxKLKzLx0hYCi_OTU4uLUYoXyzJIMBd_EvNTSstSiYh4G1rTEnOJUXijNzSDj5hri7KGbUpKZHA80Ly-1JN7RxdHQ3MjC2NyYgDQAfAktzg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>report</recordtype></control><display><type>report</type><title>An Adaptive Estimator for Time Varying Processes with Maneuvers</title><source>DTIC Technical Reports</source><creator>Schiefer,Michael A</creator><creatorcontrib>Schiefer,Michael A ; AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH</creatorcontrib><description>An adaptive discrete Kalman filter is developed which is based on an improved covariance matching technique. The filter quickly finds the unknown process noise covariance implied by the observed data. The process noise covariance is assumed constant. all other filter parameters are assumed known, although possibly time varying. This filter, used in conjunction with a multiple model filter, can be used to iteratively estimate noise statistics and detect maneuvers. (Author)</description><language>eng</language><subject>ACOUSTIC DETECTION ; ACOUSTIC MEASUREMENT ; Acoustics ; ADAPTIVE FILTERS ; CHARTS ; COVARIANCE ; Discrete filters ; ESTIMATES ; ITERATIONS ; KALMAN FILTERING ; MATCHING ; PARAMETERS ; TABLES(DATA) ; TARGET DETECTION</subject><creationdate>1986</creationdate><rights>APPROVED FOR PUBLIC RELEASE</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,776,881,27544,27545</link.rule.ids><linktorsrc>$$Uhttps://apps.dtic.mil/sti/citations/ADA172837$$EView_record_in_DTIC$$FView_record_in_$$GDTIC$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Schiefer,Michael A</creatorcontrib><creatorcontrib>AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH</creatorcontrib><title>An Adaptive Estimator for Time Varying Processes with Maneuvers</title><description>An adaptive discrete Kalman filter is developed which is based on an improved covariance matching technique. The filter quickly finds the unknown process noise covariance implied by the observed data. The process noise covariance is assumed constant. all other filter parameters are assumed known, although possibly time varying. This filter, used in conjunction with a multiple model filter, can be used to iteratively estimate noise statistics and detect maneuvers. (Author)</description><subject>ACOUSTIC DETECTION</subject><subject>ACOUSTIC MEASUREMENT</subject><subject>Acoustics</subject><subject>ADAPTIVE FILTERS</subject><subject>CHARTS</subject><subject>COVARIANCE</subject><subject>Discrete filters</subject><subject>ESTIMATES</subject><subject>ITERATIONS</subject><subject>KALMAN FILTERING</subject><subject>MATCHING</subject><subject>PARAMETERS</subject><subject>TABLES(DATA)</subject><subject>TARGET DETECTION</subject><fulltext>true</fulltext><rsrctype>report</rsrctype><creationdate>1986</creationdate><recordtype>report</recordtype><sourceid>1RU</sourceid><recordid>eNrjZLB3zFNwTEksKMksS1VwLS7JzE0syS9SSAPikMzcVIWwxKLKzLx0hYCi_OTU4uLUYoXyzJIMBd_EvNTSstSiYh4G1rTEnOJUXijNzSDj5hri7KGbUpKZHA80Ly-1JN7RxdHQ3MjC2NyYgDQAfAktzg</recordid><startdate>198608</startdate><enddate>198608</enddate><creator>Schiefer,Michael A</creator><scope>1RU</scope><scope>BHM</scope></search><sort><creationdate>198608</creationdate><title>An Adaptive Estimator for Time Varying Processes with Maneuvers</title><author>Schiefer,Michael A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-dtic_stinet_ADA1728373</frbrgroupid><rsrctype>reports</rsrctype><prefilter>reports</prefilter><language>eng</language><creationdate>1986</creationdate><topic>ACOUSTIC DETECTION</topic><topic>ACOUSTIC MEASUREMENT</topic><topic>Acoustics</topic><topic>ADAPTIVE FILTERS</topic><topic>CHARTS</topic><topic>COVARIANCE</topic><topic>Discrete filters</topic><topic>ESTIMATES</topic><topic>ITERATIONS</topic><topic>KALMAN FILTERING</topic><topic>MATCHING</topic><topic>PARAMETERS</topic><topic>TABLES(DATA)</topic><topic>TARGET DETECTION</topic><toplevel>online_resources</toplevel><creatorcontrib>Schiefer,Michael A</creatorcontrib><creatorcontrib>AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH</creatorcontrib><collection>DTIC Technical Reports</collection><collection>DTIC STINET</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Schiefer,Michael A</au><aucorp>AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH</aucorp><format>book</format><genre>unknown</genre><ristype>RPRT</ristype><btitle>An Adaptive Estimator for Time Varying Processes with Maneuvers</btitle><date>1986-08</date><risdate>1986</risdate><abstract>An adaptive discrete Kalman filter is developed which is based on an improved covariance matching technique. The filter quickly finds the unknown process noise covariance implied by the observed data. The process noise covariance is assumed constant. all other filter parameters are assumed known, although possibly time varying. This filter, used in conjunction with a multiple model filter, can be used to iteratively estimate noise statistics and detect maneuvers. (Author)</abstract><oa>free_for_read</oa></addata></record> |
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| source | DTIC Technical Reports |
| subjects | ACOUSTIC DETECTION ACOUSTIC MEASUREMENT Acoustics ADAPTIVE FILTERS CHARTS COVARIANCE Discrete filters ESTIMATES ITERATIONS KALMAN FILTERING MATCHING PARAMETERS TABLES(DATA) TARGET DETECTION |
| title | An Adaptive Estimator for Time Varying Processes with Maneuvers |
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