{"id":"https://openalex.org/W7135091080","doi":"https://doi.org/10.1109/camsap66162.2025.11423866","title":"A GLRT Detector for Correlated Targets","display_name":"A GLRT Detector for Correlated Targets","publication_year":2025,"publication_date":"2025-12-14","ids":{"openalex":"https://openalex.org/W7135091080","doi":"https://doi.org/10.1109/camsap66162.2025.11423866"},"language":null,"primary_location":{"id":"doi:10.1109/camsap66162.2025.11423866","is_oa":false,"landing_page_url":"https://doi.org/10.1109/camsap66162.2025.11423866","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 10th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","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/A5123644742","display_name":"Tomas McKelvey","orcid":null},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Tomas McKelvey","raw_affiliation_strings":["Chalmers University of Technology,Department of Electrical Engineering,Gothenburg,Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chalmers University of Technology,Department of Electrical Engineering,Gothenburg,Sweden","institution_ids":["https://openalex.org/I66862912"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008886334","display_name":"Patrik Dammert","orcid":"https://orcid.org/0000-0002-7339-849X"},"institutions":[{"id":"https://openalex.org/I4210153572","display_name":"Saab (Sweden)","ror":"https://ror.org/05kyy7d06","country_code":"SE","type":"company","lineage":["https://openalex.org/I4210153572"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Patrik Dammert","raw_affiliation_strings":["Surveillance, Saab AB Future Technologies Radar and Systems,Gothenburg,Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Surveillance, Saab AB Future Technologies Radar and Systems,Gothenburg,Sweden","institution_ids":["https://openalex.org/I4210153572"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"36","last_page":"40"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11111","display_name":"Spectroscopy and Laser Applications","score":0.14800000190734863,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11111","display_name":"Spectroscopy and Laser Applications","score":0.14800000190734863,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11044","display_name":"Particle Detector Development and Performance","score":0.051600001752376556,"subfield":{"id":"https://openalex.org/subfields/3106","display_name":"Nuclear and High Energy Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.04259999841451645,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.7099000215530396},{"id":"https://openalex.org/keywords/matched-filter","display_name":"Matched filter","score":0.6297000050544739},{"id":"https://openalex.org/keywords/amplitude","display_name":"Amplitude","score":0.6147000193595886},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5170999765396118},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5013999938964844},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5008000135421753},{"id":"https://openalex.org/keywords/likelihood-ratio-test","display_name":"Likelihood-ratio test","score":0.46140000224113464},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.4366999864578247},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.43540000915527344}],"concepts":[{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.7099000215530396},{"id":"https://openalex.org/C50151734","wikidata":"https://www.wikidata.org/wiki/Q1759577","display_name":"Matched filter","level":3,"score":0.6297000050544739},{"id":"https://openalex.org/C180205008","wikidata":"https://www.wikidata.org/wiki/Q159190","display_name":"Amplitude","level":2,"score":0.6147000193595886},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5853999853134155},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5170999765396118},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5013999938964844},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5008000135421753},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.47929999232292175},{"id":"https://openalex.org/C9483764","wikidata":"https://www.wikidata.org/wiki/Q585740","display_name":"Likelihood-ratio test","level":2,"score":0.46140000224113464},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4366999864578247},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.43540000915527344},{"id":"https://openalex.org/C169857963","wikidata":"https://www.wikidata.org/wiki/Q1461038","display_name":"Test statistic","level":3,"score":0.42980000376701355},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.4108999967575073},{"id":"https://openalex.org/C7321624","wikidata":"https://www.wikidata.org/wiki/Q205","display_name":"Infinity","level":2,"score":0.4065999984741211},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4004000127315521},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.3871000111103058},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3370000123977661},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.30979999899864197},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.2524000108242035},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2515000104904175},{"id":"https://openalex.org/C96608239","wikidata":"https://www.wikidata.org/wiki/Q1199823","display_name":"Statistical power","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/camsap66162.2025.11423866","is_oa":false,"landing_page_url":"https://doi.org/10.1109/camsap66162.2025.11423866","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 10th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,27],"detection":[1,107],"problem":[2,75,108],"for":[3,62,73],"correlated":[4,23],"targets":[5],"is":[6,11,109,119,129],"considered.":[7],"A":[8],"target":[9,18,40,106,137],"model":[10],"employed":[12],"that":[13,68,115],"encompass":[14],"a":[15,22,46,55,104,135],"time":[16,85,97],"varying":[17],"amplitude":[19,41,138],"described":[20],"as":[21,82,94],"Gaussian":[24],"stochastic":[25],"process.":[26],"likelihood":[28],"ratio":[29],"test":[30,71],"(LRT),":[31],"Detection":[32],"of":[33],"Correlated":[34,63],"Targets":[35,64],"(DCT)":[36],"depends":[37],"on":[38],"the":[39,50,59,69,74,78,83,91,95,112,116,124,132],"variance":[42,51],"and":[43,52,89,128],"we":[44],"present":[45],"method":[47],"to":[48,77,87,90,99,123],"derive":[49],"arrive":[53],"at":[54],"generalized":[56],"LRT-statistic":[57],"called":[58],"Generalised":[60],"Detector":[61],"(GDCT).":[65],"We":[66],"show":[67],"LRT":[70],"statistic":[72],"converges":[76],"classical":[79],"matched":[80],"filter":[81],"correlation":[84,96],"goes":[86,98],"infinity":[88],"energy":[92],"detector":[93],"zero.":[100],"An":[101],"example":[102],"mimicking":[103],"moving":[105],"presented":[110],"where":[111],"result":[113],"illustrates":[114],"GDCT":[117],"performance":[118],"only":[120],"slightly":[121],"inferior":[122],"correctly":[125],"specified":[126],"DCT":[127,133],"superior":[130],"if":[131],"has":[134],"miss-specified":[136],"variance.":[139]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-03-13T00:00:00"}