Greedy Selection of Sensors for Linear Bayesian Estimation under Correlated Noise
We consider the problem of finding the best subset of sensors in wireless sensor networks where linear Bayesian parameter estimation is conducted from the selected measurements corrupted by correlated noise. We aim to directly minimize the estimation error which is manipulated by using the QR and LU...
Gespeichert in:
| Veröffentlicht in: | IEICE Transactions on Information and Systems 2024/09/01, Vol.E107.D(9), pp.1274-1277 |
|---|---|
| 1. Verfasser: | |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1277 |
|---|---|
| container_issue | 9 |
| container_start_page | 1274 |
| container_title | IEICE Transactions on Information and Systems |
| container_volume | E107.D |
| creator | KIM, Yoon Hak |
| description | We consider the problem of finding the best subset of sensors in wireless sensor networks where linear Bayesian parameter estimation is conducted from the selected measurements corrupted by correlated noise. We aim to directly minimize the estimation error which is manipulated by using the QR and LU factorizations. We derive an analytic result which expedites the sensor selection in a greedy manner. We also provide the complexity of the proposed algorithm in comparison with previous selection methods. We evaluate the performance through numerical experiments using random measurements under correlated noise and demonstrate a competitive estimation accuracy of the proposed algorithm with a reasonable increase in complexity as compared with the previous selection methods. |
| doi_str_mv | 10.1587/transinf.2024EDL8024 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3116526076</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3116526076</sourcerecordid><originalsourceid>FETCH-LOGICAL-c402t-c92fd756abf9988bb13f9e60b8fb6d67bc24df9924e2fb995b623baf451b71433</originalsourceid><addsrcrecordid>eNpNkMtOwzAQRS0EEqXwBywisQ74nXgJbSlIEYjX2rKTMaQKdrHTRf-eQGnpamY0594ZXYTOCb4koiyu-mh8ar27pJjy2bQqh3KARqTgIidMkkM0worIvBSMHqOTlBYYk5ISMUJP8wjQrLMX6KDu2-Cz4IbBpxBT5kLMqtaDidmNWUNqjc9mqW8_zS-58g3EbBJihM700GQPoU1wio6c6RKc_dUxerudvU7u8upxfj-5rvKaY9rntaKuKYQ01ilVltYS5hRIbEtnZSMLW1PeDCvKgTqrlLCSMmscF8QWhDM2Rhcb32UMXytIvV6EVfTDSc0IkYJKXMiB4huqjiGlCE4v4_B_XGuC9U94ehue3gtvkD1vZIvUm3fYiUzs27qDf9GM4EJPtdo2eyY7uP4wUYNn366rglo</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3116526076</pqid></control><display><type>article</type><title>Greedy Selection of Sensors for Linear Bayesian Estimation under Correlated Noise</title><source>J-STAGE (Japan Science & Technology Information Aggregator, Electronic) Freely Available Titles - Japanese</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>KIM, Yoon Hak</creator><creatorcontrib>KIM, Yoon Hak</creatorcontrib><description>We consider the problem of finding the best subset of sensors in wireless sensor networks where linear Bayesian parameter estimation is conducted from the selected measurements corrupted by correlated noise. We aim to directly minimize the estimation error which is manipulated by using the QR and LU factorizations. We derive an analytic result which expedites the sensor selection in a greedy manner. We also provide the complexity of the proposed algorithm in comparison with previous selection methods. We evaluate the performance through numerical experiments using random measurements under correlated noise and demonstrate a competitive estimation accuracy of the proposed algorithm with a reasonable increase in complexity as compared with the previous selection methods.</description><identifier>ISSN: 0916-8532</identifier><identifier>EISSN: 1745-1361</identifier><identifier>DOI: 10.1587/transinf.2024EDL8024</identifier><language>eng</language><publisher>Tokyo: The Institute of Electronics, Information and Communication Engineers</publisher><subject>Bayesian analysis ; Complexity ; Correlation ; Error analysis ; greedy algorithm ; Greedy algorithms ; linear Bayesian estimation ; LU factorization ; Parameter estimation ; QR factorization ; sensor selection ; Sensors ; Wireless sensor networks</subject><ispartof>IEICE Transactions on Information and Systems, 2024/09/01, Vol.E107.D(9), pp.1274-1277</ispartof><rights>2024 The Institute of Electronics, Information and Communication Engineers</rights><rights>Copyright Japan Science and Technology Agency 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c402t-c92fd756abf9988bb13f9e60b8fb6d67bc24df9924e2fb995b623baf451b71433</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,1883,27924,27925</link.rule.ids></links><search><creatorcontrib>KIM, Yoon Hak</creatorcontrib><title>Greedy Selection of Sensors for Linear Bayesian Estimation under Correlated Noise</title><title>IEICE Transactions on Information and Systems</title><addtitle>IEICE Trans. Inf. & Syst.</addtitle><description>We consider the problem of finding the best subset of sensors in wireless sensor networks where linear Bayesian parameter estimation is conducted from the selected measurements corrupted by correlated noise. We aim to directly minimize the estimation error which is manipulated by using the QR and LU factorizations. We derive an analytic result which expedites the sensor selection in a greedy manner. We also provide the complexity of the proposed algorithm in comparison with previous selection methods. We evaluate the performance through numerical experiments using random measurements under correlated noise and demonstrate a competitive estimation accuracy of the proposed algorithm with a reasonable increase in complexity as compared with the previous selection methods.</description><subject>Bayesian analysis</subject><subject>Complexity</subject><subject>Correlation</subject><subject>Error analysis</subject><subject>greedy algorithm</subject><subject>Greedy algorithms</subject><subject>linear Bayesian estimation</subject><subject>LU factorization</subject><subject>Parameter estimation</subject><subject>QR factorization</subject><subject>sensor selection</subject><subject>Sensors</subject><subject>Wireless sensor networks</subject><issn>0916-8532</issn><issn>1745-1361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNpNkMtOwzAQRS0EEqXwBywisQ74nXgJbSlIEYjX2rKTMaQKdrHTRf-eQGnpamY0594ZXYTOCb4koiyu-mh8ar27pJjy2bQqh3KARqTgIidMkkM0worIvBSMHqOTlBYYk5ISMUJP8wjQrLMX6KDu2-Cz4IbBpxBT5kLMqtaDidmNWUNqjc9mqW8_zS-58g3EbBJihM700GQPoU1wio6c6RKc_dUxerudvU7u8upxfj-5rvKaY9rntaKuKYQ01ilVltYS5hRIbEtnZSMLW1PeDCvKgTqrlLCSMmscF8QWhDM2Rhcb32UMXytIvV6EVfTDSc0IkYJKXMiB4huqjiGlCE4v4_B_XGuC9U94ehue3gtvkD1vZIvUm3fYiUzs27qDf9GM4EJPtdo2eyY7uP4wUYNn366rglo</recordid><startdate>20240901</startdate><enddate>20240901</enddate><creator>KIM, Yoon Hak</creator><general>The Institute of Electronics, Information and Communication Engineers</general><general>Japan Science and Technology Agency</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20240901</creationdate><title>Greedy Selection of Sensors for Linear Bayesian Estimation under Correlated Noise</title><author>KIM, Yoon Hak</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c402t-c92fd756abf9988bb13f9e60b8fb6d67bc24df9924e2fb995b623baf451b71433</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Bayesian analysis</topic><topic>Complexity</topic><topic>Correlation</topic><topic>Error analysis</topic><topic>greedy algorithm</topic><topic>Greedy algorithms</topic><topic>linear Bayesian estimation</topic><topic>LU factorization</topic><topic>Parameter estimation</topic><topic>QR factorization</topic><topic>sensor selection</topic><topic>Sensors</topic><topic>Wireless sensor networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>KIM, Yoon Hak</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research 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>IEICE Transactions on Information and Systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>KIM, Yoon Hak</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Greedy Selection of Sensors for Linear Bayesian Estimation under Correlated Noise</atitle><jtitle>IEICE Transactions on Information and Systems</jtitle><addtitle>IEICE Trans. Inf. & Syst.</addtitle><date>2024-09-01</date><risdate>2024</risdate><volume>E107.D</volume><issue>9</issue><spage>1274</spage><epage>1277</epage><pages>1274-1277</pages><artnum>2024EDL8024</artnum><issn>0916-8532</issn><eissn>1745-1361</eissn><abstract>We consider the problem of finding the best subset of sensors in wireless sensor networks where linear Bayesian parameter estimation is conducted from the selected measurements corrupted by correlated noise. We aim to directly minimize the estimation error which is manipulated by using the QR and LU factorizations. We derive an analytic result which expedites the sensor selection in a greedy manner. We also provide the complexity of the proposed algorithm in comparison with previous selection methods. We evaluate the performance through numerical experiments using random measurements under correlated noise and demonstrate a competitive estimation accuracy of the proposed algorithm with a reasonable increase in complexity as compared with the previous selection methods.</abstract><cop>Tokyo</cop><pub>The Institute of Electronics, Information and Communication Engineers</pub><doi>10.1587/transinf.2024EDL8024</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0916-8532 |
| ispartof | IEICE Transactions on Information and Systems, 2024/09/01, Vol.E107.D(9), pp.1274-1277 |
| issn | 0916-8532 1745-1361 |
| language | eng |
| recordid | cdi_proquest_journals_3116526076 |
| source | J-STAGE (Japan Science & Technology Information Aggregator, Electronic) Freely Available Titles - Japanese; EZB-FREE-00999 freely available EZB journals |
| subjects | Bayesian analysis Complexity Correlation Error analysis greedy algorithm Greedy algorithms linear Bayesian estimation LU factorization Parameter estimation QR factorization sensor selection Sensors Wireless sensor networks |
| title | Greedy Selection of Sensors for Linear Bayesian Estimation under Correlated Noise |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T11%3A01%3A27IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Greedy%20Selection%20of%20Sensors%20for%20Linear%20Bayesian%20Estimation%20under%20Correlated%20Noise&rft.jtitle=IEICE%20Transactions%20on%20Information%20and%20Systems&rft.au=KIM,%20Yoon%20Hak&rft.date=2024-09-01&rft.volume=E107.D&rft.issue=9&rft.spage=1274&rft.epage=1277&rft.pages=1274-1277&rft.artnum=2024EDL8024&rft.issn=0916-8532&rft.eissn=1745-1361&rft_id=info:doi/10.1587/transinf.2024EDL8024&rft_dat=%3Cproquest_cross%3E3116526076%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3116526076&rft_id=info:pmid/&rfr_iscdi=true |