{"id":"https://openalex.org/W1964626789","doi":"https://doi.org/10.1109/sam.2012.6250547","title":"The locally most powerful invariant test for detecting a rank-P Gaussian signal in white noise","display_name":"The locally most powerful invariant test for detecting a rank-P Gaussian signal in white noise","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W1964626789","doi":"https://doi.org/10.1109/sam.2012.6250547","mag":"1964626789"},"language":"en","primary_location":{"id":"doi:10.1109/sam.2012.6250547","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sam.2012.6250547","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058914830","display_name":"David Ram\u00edrez","orcid":"https://orcid.org/0000-0002-6883-7742"},"institutions":[{"id":"https://openalex.org/I206945453","display_name":"Paderborn University","ror":"https://ror.org/058kzsd48","country_code":"DE","type":"education","lineage":["https://openalex.org/I206945453"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"David Ramirez","raw_affiliation_strings":["Department of Electrical Engineering and Information Technology, Universit\u00e4t Paderborn, Paderborn, Germany","Dept. of Electrical Engineering and Information Technology, Universit\u00e4t Paderborn, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Information Technology, Universit\u00e4t Paderborn, Paderborn, Germany","institution_ids":["https://openalex.org/I206945453"]},{"raw_affiliation_string":"Dept. of Electrical Engineering and Information Technology, Universit\u00e4t Paderborn, Germany","institution_ids":["https://openalex.org/I206945453"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042673887","display_name":"Jorge Iscar","orcid":"https://orcid.org/0000-0002-6471-1659"},"institutions":[{"id":"https://openalex.org/I13134134","display_name":"Universidad de Cantabria","ror":"https://ror.org/046ffzj20","country_code":"ES","type":"education","lineage":["https://openalex.org/I13134134"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Jorge Iscar","raw_affiliation_strings":["Department of Communications Engineering, University of Cantabria, Santander, Spain","Department of Communications Engineering, University of Cantabria , Santander , Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Communications Engineering, University of Cantabria, Santander, Spain","institution_ids":["https://openalex.org/I13134134"]},{"raw_affiliation_string":"Department of Communications Engineering, University of Cantabria , Santander , Spain","institution_ids":["https://openalex.org/I13134134"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071269640","display_name":"Javier V\u00eda","orcid":"https://orcid.org/0000-0001-7853-0069"},"institutions":[{"id":"https://openalex.org/I13134134","display_name":"Universidad de Cantabria","ror":"https://ror.org/046ffzj20","country_code":"ES","type":"education","lineage":["https://openalex.org/I13134134"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Javier Via","raw_affiliation_strings":["Department of Communications Engineering, University of Cantabria, Santander, Spain","Department of Communications Engineering, University of Cantabria , Santander , Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Communications Engineering, University of Cantabria, Santander, Spain","institution_ids":["https://openalex.org/I13134134"]},{"raw_affiliation_string":"Department of Communications Engineering, University of Cantabria , Santander , Spain","institution_ids":["https://openalex.org/I13134134"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059078809","display_name":"Ignacio Santamar\u0131\u0301a","orcid":"https://orcid.org/0000-0003-0040-7436"},"institutions":[{"id":"https://openalex.org/I13134134","display_name":"Universidad de Cantabria","ror":"https://ror.org/046ffzj20","country_code":"ES","type":"education","lineage":["https://openalex.org/I13134134"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Ignacio Santamaria","raw_affiliation_strings":["Department of Communications Engineering, University of Cantabria, Santander, Spain","Department of Communications Engineering, University of Cantabria , Santander , Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Communications Engineering, University of Cantabria, Santander, Spain","institution_ids":["https://openalex.org/I13134134"]},{"raw_affiliation_string":"Department of Communications Engineering, University of Cantabria , Santander , Spain","institution_ids":["https://openalex.org/I13134134"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080469112","display_name":"Louis L. Scharf","orcid":"https://orcid.org/0000-0003-1764-9335"},"institutions":[{"id":"https://openalex.org/I92446798","display_name":"Colorado State University","ror":"https://ror.org/03k1gpj17","country_code":"US","type":"education","lineage":["https://openalex.org/I92446798"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Louis L. Scharf","raw_affiliation_strings":["Department of Mathematics and Statistics, Colorado State University, Fort Collins, USA","Depts. of Mathematics and Statistics, Colorado State University, Fort Collins, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, Colorado State University, Fort Collins, USA","institution_ids":["https://openalex.org/I92446798"]},{"raw_affiliation_string":"Depts. of Mathematics and Statistics, Colorado State University, Fort Collins, USA","institution_ids":["https://openalex.org/I92446798"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"493","last_page":"496"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10579","display_name":"Cognitive Radio Networks and Spectrum Sensing","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9955999851226807,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6976515054702759},{"id":"https://openalex.org/keywords/likelihood-ratio-test","display_name":"Likelihood-ratio test","score":0.6590685844421387},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.5769984722137451},{"id":"https://openalex.org/keywords/test-statistic","display_name":"Test statistic","score":0.5730576515197754},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5663150548934937},{"id":"https://openalex.org/keywords/noise-power","display_name":"Noise power","score":0.5412530303001404},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.5115845799446106},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.4925471246242523},{"id":"https://openalex.org/keywords/detection-theory","display_name":"Detection theory","score":0.47468873858451843},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4649431109428406},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.4548538029193878},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"Signal-to-noise ratio (imaging)","score":0.42750898003578186},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.39748701453208923},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3936513364315033},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.2904377579689026},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2542257308959961},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.18480026721954346},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.13385841250419617},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.0975765585899353}],"concepts":[{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6976515054702759},{"id":"https://openalex.org/C9483764","wikidata":"https://www.wikidata.org/wiki/Q585740","display_name":"Likelihood-ratio test","level":2,"score":0.6590685844421387},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.5769984722137451},{"id":"https://openalex.org/C169857963","wikidata":"https://www.wikidata.org/wiki/Q1461038","display_name":"Test statistic","level":3,"score":0.5730576515197754},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5663150548934937},{"id":"https://openalex.org/C203234222","wikidata":"https://www.wikidata.org/wiki/Q2133519","display_name":"Noise power","level":3,"score":0.5412530303001404},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.5115845799446106},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4925471246242523},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.47468873858451843},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4649431109428406},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.4548538029193878},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.42750898003578186},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39748701453208923},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3936513364315033},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2904377579689026},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2542257308959961},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.18480026721954346},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.13385841250419617},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0975765585899353},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sam.2012.6250547","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sam.2012.6250547","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1536100164","https://openalex.org/W1540313536","https://openalex.org/W1578953027","https://openalex.org/W1980455221","https://openalex.org/W2036248065","https://openalex.org/W2118880464","https://openalex.org/W2163520367","https://openalex.org/W2163852636","https://openalex.org/W2168078104","https://openalex.org/W2172139273","https://openalex.org/W4233959632","https://openalex.org/W6631820835"],"related_works":["https://openalex.org/W2027428453","https://openalex.org/W2371289173","https://openalex.org/W2584466131","https://openalex.org/W2015579740","https://openalex.org/W2015465174","https://openalex.org/W2031443741","https://openalex.org/W2408444874","https://openalex.org/W2155687826","https://openalex.org/W2170302359","https://openalex.org/W2294267340"],"abstract_inverted_index":{"Spectrum":[0],"sensing":[1],"has":[2],"become":[3],"one":[4],"of":[5,9,38,103,160],"the":[6,33,39,50,72,81,101,104,125,133,135,141,157,161],"main":[7],"components":[8],"a":[10,91],"cognitive":[11],"transmitter.":[12],"Conventional":[13],"detectors":[14,22,42],"suffer":[15],"from":[16],"noise":[17],"power":[18],"uncertainties":[19],"and":[20,30,129],"multiantenna":[21,41],"have":[23],"been":[24],"proposed":[25,40],"to":[26,31,111,132,155],"overcome":[27],"this":[28,68],"difficulty,":[29],"improve":[32],"detection":[34],"performance.":[35],"However,":[36],"most":[37,74],"are":[43,61],"based":[44,63,99],"on":[45,64,100],"non-optimal":[46],"techniques,":[47],"such":[48],"as":[49],"generalized":[51],"likelihood":[52],"ratio":[53,94],"test":[54,77],"(GLRT),":[55],"or":[56,88],"even":[57],"heuristic":[58],"approaches":[59],"that":[60,79],"not":[62,116],"first":[65],"principles.":[66],"In":[67],"work,":[69],"we":[70,150],"derive":[71],"locally":[73],"powerful":[75],"invariant":[76,83,106],"(LMPIT),":[78],"is,":[80],"optimal":[82,147],"detector":[84],"for":[85,90,146],"close":[86],"hypotheses,":[87],"equivalently,":[89],"low":[92,126],"signal-to-noise":[93],"(SNR).":[95],"The":[96],"traditional":[97],"approach,":[98],"distributions":[102],"maximal":[105],"statistic,":[107],"is":[108,144],"avoided":[109],"thanks":[110],"Wijsman's":[112],"theorem,":[113],"which":[114],"does":[115],"need":[117],"these":[118],"distributions.":[119],"Our":[120],"findings":[121],"show":[122],"that,":[123],"in":[124,130],"SNR":[127],"regime,":[128],"contrast":[131],"GLRT,":[134],"additional":[136],"spatial":[137],"structure":[138],"imposed":[139],"by":[140],"signal":[142],"model":[143],"irrelevant":[145],"detection.":[148],"Finally,":[149],"use":[151],"Monte":[152],"Carlo":[153],"simulations":[154],"illustrate":[156],"good":[158],"performance":[159],"LMPIT.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
