Descriptor Kalman estimators
A unifying framework of steady-state Kalman filtering, smoothing and prediction for descriptor systems is presented by using the innovation analysis method in the time domain. The descriptor Kalman estimators are presented on the basis of the autoregressive moving-average innovation model and white-...
Gespeichert in:
| Veröffentlicht in: | International journal of systems science 1999, Vol.30 (11), p.1205-1212 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1212 |
|---|---|
| container_issue | 11 |
| container_start_page | 1205 |
| container_title | International journal of systems science |
| container_volume | 30 |
| creator | Deng, Zi-Li Liu, Yu-Mei |
| description | A unifying framework of steady-state Kalman filtering, smoothing and prediction for descriptor systems is presented by using the innovation analysis method in the time domain. The descriptor Kalman estimators are presented on the basis of the autoregressive moving-average innovation model and white-noise estimators. The new algorithms of steady-state descriptor Kalman estimators gains are given. The solution of the Riccati equation is avoided. To ensure the asymptotic stability of descriptor Kalman estimators with respect to the initial values of innovation process, formulae for selecting their initial values are given. A simulation example shows the usefulness of the proposed results. |
| doi_str_mv | 10.1080/002077299291679 |
| format | Article |
| fullrecord | <record><control><sourceid>crossref_infor</sourceid><recordid>TN_cdi_crossref_primary_10_1080_002077299291679</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_1080_002077299291679</sourcerecordid><originalsourceid>FETCH-LOGICAL-c336t-8c4c064dda189e9cbc04ec130bd1ffd0f61e84b5151f3f2e01e82625947059553</originalsourceid><addsrcrecordid>eNqFj81LAzEQxYMouFbPXjz0H1g7k6_deJNqtbTgRc8hm01gZT9KEtD-96bUU0E8DTOP33vzCLlFuEeoYQFAoaqoUlShrNQZKZBLXgqG6pwUB7XMMl6Sqxg_AUAICgW5e3LRhm6XpjDfmH4w49zF1A0mH-I1ufCmj-7md87Ix-r5fflabt9e1svHbWkZk6msLbcgedsarJVTtrHAnUUGTYvet-Alupo3AgV65qmDvFJJheIVCCUEm5HF0deGKcbgvN6F_ELYawR9KKdPymVCHIlu9FMYzNcU-lYns--n4IMZbRdPGZ2-U-Ye_uXYX6E_NYVkpg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Descriptor Kalman estimators</title><source>Taylor & Francis</source><creator>Deng, Zi-Li ; Liu, Yu-Mei</creator><creatorcontrib>Deng, Zi-Li ; Liu, Yu-Mei</creatorcontrib><description>A unifying framework of steady-state Kalman filtering, smoothing and prediction for descriptor systems is presented by using the innovation analysis method in the time domain. The descriptor Kalman estimators are presented on the basis of the autoregressive moving-average innovation model and white-noise estimators. The new algorithms of steady-state descriptor Kalman estimators gains are given. The solution of the Riccati equation is avoided. To ensure the asymptotic stability of descriptor Kalman estimators with respect to the initial values of innovation process, formulae for selecting their initial values are given. A simulation example shows the usefulness of the proposed results.</description><identifier>ISSN: 0020-7721</identifier><identifier>EISSN: 1464-5319</identifier><identifier>DOI: 10.1080/002077299291679</identifier><language>eng</language><publisher>Taylor & Francis Group</publisher><ispartof>International journal of systems science, 1999, Vol.30 (11), p.1205-1212</ispartof><rights>Copyright Taylor & Francis Group, LLC 1999</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c336t-8c4c064dda189e9cbc04ec130bd1ffd0f61e84b5151f3f2e01e82625947059553</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.tandfonline.com/doi/pdf/10.1080/002077299291679$$EPDF$$P50$$Ginformaworld$$H</linktopdf><linktohtml>$$Uhttps://www.tandfonline.com/doi/full/10.1080/002077299291679$$EHTML$$P50$$Ginformaworld$$H</linktohtml><link.rule.ids>314,776,780,4010,27900,27901,27902,59620,60409</link.rule.ids></links><search><creatorcontrib>Deng, Zi-Li</creatorcontrib><creatorcontrib>Liu, Yu-Mei</creatorcontrib><title>Descriptor Kalman estimators</title><title>International journal of systems science</title><description>A unifying framework of steady-state Kalman filtering, smoothing and prediction for descriptor systems is presented by using the innovation analysis method in the time domain. The descriptor Kalman estimators are presented on the basis of the autoregressive moving-average innovation model and white-noise estimators. The new algorithms of steady-state descriptor Kalman estimators gains are given. The solution of the Riccati equation is avoided. To ensure the asymptotic stability of descriptor Kalman estimators with respect to the initial values of innovation process, formulae for selecting their initial values are given. A simulation example shows the usefulness of the proposed results.</description><issn>0020-7721</issn><issn>1464-5319</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><recordid>eNqFj81LAzEQxYMouFbPXjz0H1g7k6_deJNqtbTgRc8hm01gZT9KEtD-96bUU0E8DTOP33vzCLlFuEeoYQFAoaqoUlShrNQZKZBLXgqG6pwUB7XMMl6Sqxg_AUAICgW5e3LRhm6XpjDfmH4w49zF1A0mH-I1ufCmj-7md87Ix-r5fflabt9e1svHbWkZk6msLbcgedsarJVTtrHAnUUGTYvet-Alupo3AgV65qmDvFJJheIVCCUEm5HF0deGKcbgvN6F_ELYawR9KKdPymVCHIlu9FMYzNcU-lYns--n4IMZbRdPGZ2-U-Ye_uXYX6E_NYVkpg</recordid><startdate>1999</startdate><enddate>1999</enddate><creator>Deng, Zi-Li</creator><creator>Liu, Yu-Mei</creator><general>Taylor & Francis Group</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>1999</creationdate><title>Descriptor Kalman estimators</title><author>Deng, Zi-Li ; Liu, Yu-Mei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c336t-8c4c064dda189e9cbc04ec130bd1ffd0f61e84b5151f3f2e01e82625947059553</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1999</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Deng, Zi-Li</creatorcontrib><creatorcontrib>Liu, Yu-Mei</creatorcontrib><collection>CrossRef</collection><jtitle>International journal of systems science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Deng, Zi-Li</au><au>Liu, Yu-Mei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Descriptor Kalman estimators</atitle><jtitle>International journal of systems science</jtitle><date>1999</date><risdate>1999</risdate><volume>30</volume><issue>11</issue><spage>1205</spage><epage>1212</epage><pages>1205-1212</pages><issn>0020-7721</issn><eissn>1464-5319</eissn><abstract>A unifying framework of steady-state Kalman filtering, smoothing and prediction for descriptor systems is presented by using the innovation analysis method in the time domain. The descriptor Kalman estimators are presented on the basis of the autoregressive moving-average innovation model and white-noise estimators. The new algorithms of steady-state descriptor Kalman estimators gains are given. The solution of the Riccati equation is avoided. To ensure the asymptotic stability of descriptor Kalman estimators with respect to the initial values of innovation process, formulae for selecting their initial values are given. A simulation example shows the usefulness of the proposed results.</abstract><pub>Taylor & Francis Group</pub><doi>10.1080/002077299291679</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0020-7721 |
| ispartof | International journal of systems science, 1999, Vol.30 (11), p.1205-1212 |
| issn | 0020-7721 1464-5319 |
| language | eng |
| recordid | cdi_crossref_primary_10_1080_002077299291679 |
| source | Taylor & Francis |
| title | Descriptor Kalman estimators |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-05T08%3A36%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-crossref_infor&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Descriptor%20Kalman%20estimators&rft.jtitle=International%20journal%20of%20systems%20science&rft.au=Deng,%20Zi-Li&rft.date=1999&rft.volume=30&rft.issue=11&rft.spage=1205&rft.epage=1212&rft.pages=1205-1212&rft.issn=0020-7721&rft.eissn=1464-5319&rft_id=info:doi/10.1080/002077299291679&rft_dat=%3Ccrossref_infor%3E10_1080_002077299291679%3C/crossref_infor%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |