{"id":"https://openalex.org/W231899087","doi":"https://doi.org/10.1109/sam.2016.7569686","title":"A subspace method for array covariance matrix estimation","display_name":"A subspace method for array covariance matrix estimation","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W231899087","doi":"https://doi.org/10.1109/sam.2016.7569686","mag":"231899087"},"language":"en","primary_location":{"id":"doi:10.1109/sam.2016.7569686","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sam.2016.7569686","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1411.0622","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101657195","display_name":"Mostafa Rahmani","orcid":"https://orcid.org/0000-0002-4140-383X"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mostafa Rahmani","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, USA","Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Fl., USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Fl., USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003612688","display_name":"George Atia","orcid":"https://orcid.org/0000-0001-7958-9855"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"George K. Atia","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, USA","Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Fl., USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Fl., USA","institution_ids":["https://openalex.org/I106165777"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101657195"],"corresponding_institution_ids":["https://openalex.org/I106165777"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.00046811,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11946","display_name":"Antenna Design and Optimization","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.7256370782852173},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.7005125880241394},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6914710998535156},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.684523344039917},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.6248201727867126},{"id":"https://openalex.org/keywords/covariance-intersection","display_name":"Covariance intersection","score":0.6223185658454895},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.6190726161003113},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6013418436050415},{"id":"https://openalex.org/keywords/law-of-total-covariance","display_name":"Law of total covariance","score":0.5653022527694702},{"id":"https://openalex.org/keywords/rational-quadratic-covariance-function","display_name":"Rational quadratic covariance function","score":0.5531041026115417},{"id":"https://openalex.org/keywords/mat\u00e9rn-covariance-function","display_name":"Mat\u00e9rn covariance function","score":0.5281652808189392},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4779524803161621},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.45658960938453674},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.44793617725372314},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3858846426010132},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15823501348495483},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.13128727674484253},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.06976696848869324}],"concepts":[{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.7256370782852173},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.7005125880241394},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6914710998535156},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.684523344039917},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6248201727867126},{"id":"https://openalex.org/C83042196","wikidata":"https://www.wikidata.org/wiki/Q5178898","display_name":"Covariance intersection","level":4,"score":0.6223185658454895},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.6190726161003113},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6013418436050415},{"id":"https://openalex.org/C126372606","wikidata":"https://www.wikidata.org/wiki/Q6503511","display_name":"Law of total covariance","level":5,"score":0.5653022527694702},{"id":"https://openalex.org/C148893098","wikidata":"https://www.wikidata.org/wiki/Q7295778","display_name":"Rational quadratic covariance function","level":5,"score":0.5531041026115417},{"id":"https://openalex.org/C118006245","wikidata":"https://www.wikidata.org/wiki/Q6792079","display_name":"Mat\u00e9rn covariance function","level":5,"score":0.5281652808189392},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4779524803161621},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45658960938453674},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.44793617725372314},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3858846426010132},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15823501348495483},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.13128727674484253},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.06976696848869324},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/sam.2016.7569686","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sam.2016.7569686","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1411.0622","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1411.0622","pdf_url":"https://arxiv.org/pdf/1411.0622","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:231899087","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1411.0622.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:stars.library.ucf.edu:scopus2015-4988","is_oa":true,"landing_page_url":"https://stars.library.ucf.edu/scopus2015/3989","pdf_url":null,"source":{"id":"https://openalex.org/S4210172555","display_name":"Journal of International Crisis and Risk Communication Research","issn_l":"2576-0017","issn":["2576-0017","2576-0025"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Scopus Export 2015-2019","raw_type":"text"},{"id":"doi:10.48550/arxiv.1411.0622","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1411.0622","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1411.0622","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1411.0622","pdf_url":"https://arxiv.org/pdf/1411.0622","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1262555466","display_name":null,"funder_award_id":"1320547","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6266848605","display_name":null,"funder_award_id":"CCF-1320547","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W231899087.pdf","grobid_xml":"https://content.openalex.org/works/W231899087.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W653761051","https://openalex.org/W2009850603","https://openalex.org/W2018357642","https://openalex.org/W2019945856","https://openalex.org/W2036185649","https://openalex.org/W2044000385","https://openalex.org/W2077813311","https://openalex.org/W2097469697","https://openalex.org/W2098174516","https://openalex.org/W2103519107","https://openalex.org/W2113638573","https://openalex.org/W2115141757","https://openalex.org/W2121367139","https://openalex.org/W2138638869","https://openalex.org/W2153103789","https://openalex.org/W2155413294","https://openalex.org/W2165941627","https://openalex.org/W6675979915","https://openalex.org/W6686276560","https://openalex.org/W6694535546","https://openalex.org/W6929283985"],"related_works":["https://openalex.org/W2963166347","https://openalex.org/W2157532148","https://openalex.org/W2138981061","https://openalex.org/W2013233350","https://openalex.org/W1554385044","https://openalex.org/W2895824799","https://openalex.org/W2773200628","https://openalex.org/W2010530109","https://openalex.org/W2767933251","https://openalex.org/W2166963913","https://openalex.org/W2259656359","https://openalex.org/W2745001417","https://openalex.org/W2051253827","https://openalex.org/W2039244422","https://openalex.org/W164975227","https://openalex.org/W2609219981","https://openalex.org/W2078038009","https://openalex.org/W2966883281","https://openalex.org/W2170866857","https://openalex.org/W3170640779"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"a":[3,30,58,66,69,82],"subspace":[4,32,118],"method":[5],"for":[6],"the":[7,15,24,40,51,75,109,117,120,129,135,139],"estimation":[8,101,110,136],"of":[9,39,119,138],"an":[10],"array":[11,25],"covariance":[12,26,60,122,140],"matrix.":[13,141],"When":[14],"received":[16,41],"signals":[17,42],"are":[18],"uncorrelated,":[19],"it":[20,107],"is":[21,46,63,88,91,96],"shown":[22,97],"that":[23,87,112,128],"matrices":[27],"lie":[28,115],"in":[29,116],"special":[31],"defined":[33],"through":[34],"all":[35],"possible":[36],"correlation":[37],"vectors":[38],"and":[43],"whose":[44],"dimension":[45],"typically":[47],"much":[48],"smaller":[49],"than":[50,103],"ambient":[52],"dimension.":[53],"Based":[54],"on":[55],"this":[56],"observation,":[57],"subspace-based":[59],"matrix":[61],"estimator":[62,131],"proposed":[64,94,130],"as":[65],"solution":[67,86],"to":[68,98],"semi-definite":[70],"convex":[71],"optimization":[72,76],"problem.":[73],"While":[74],"problem":[77],"has":[78],"no":[79],"closed-form":[80,85],"solution,":[81],"nearly":[83],"optimal":[84],"easily":[89],"implementable":[90],"proposed.":[92],"The":[93,124],"approach":[95],"yield":[99],"higher":[100],"accuracy":[102],"conventional":[104],"approaches":[105],"since":[106],"eliminates":[108],"error":[111],"does":[113],"not":[114],"true":[121],"matrices.":[123],"numerical":[125],"examples":[126],"demonstrate":[127],"can":[132],"significantly":[133],"improve":[134],"quality":[137]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
