{"id":"https://openalex.org/W3080142519","doi":"https://doi.org/10.1109/taes.2020.3018900","title":"Measurement Extraction of Two Targets With Unequal and Unknown Intensities in an FPA","display_name":"Measurement Extraction of Two Targets With Unequal and Unknown Intensities in an FPA","publication_year":2020,"publication_date":"2020-08-24","ids":{"openalex":"https://openalex.org/W3080142519","doi":"https://doi.org/10.1109/taes.2020.3018900","mag":"3080142519"},"language":"en","primary_location":{"id":"doi:10.1109/taes.2020.3018900","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taes.2020.3018900","pdf_url":null,"source":{"id":"https://openalex.org/S193624734","display_name":"IEEE Transactions on Aerospace and Electronic Systems","issn_l":"0018-9251","issn":["0018-9251","1557-9603","2371-9877"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Aerospace and Electronic Systems","raw_type":"journal-article"},"type":"article","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/A5088702307","display_name":"Andrew Finelli","orcid":"https://orcid.org/0000-0002-5431-3577"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Andrew Finelli","raw_affiliation_strings":["University of Connecticut, Storrs, USA"],"affiliations":[{"raw_affiliation_string":"University of Connecticut, Storrs, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044127482","display_name":"Yaakov Bar\u2010Shalom","orcid":"https://orcid.org/0000-0003-1317-3368"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yaakov Bar-Shalom","raw_affiliation_strings":["University of Connecticut, Storrs, USA"],"affiliations":[{"raw_affiliation_string":"University of Connecticut, Storrs, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019392883","display_name":"Peter Willett","orcid":"https://orcid.org/0000-0001-8443-5586"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peter Willett","raw_affiliation_strings":["University of Connecticut, Storrs, USA"],"affiliations":[{"raw_affiliation_string":"University of Connecticut, Storrs, USA","institution_ids":["https://openalex.org/I140172145"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088702307"],"corresponding_institution_ids":["https://openalex.org/I140172145"],"apc_list":null,"apc_paid":null,"fwci":1.0355,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.81956886,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"57","issue":"1","first_page":"437","last_page":"447"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9988999962806702,"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"}},{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9980999827384949,"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"}},{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/centroid","display_name":"Centroid","score":0.8558254241943359},{"id":"https://openalex.org/keywords/cram\u00e9r\u2013rao-bound","display_name":"Cram\u00e9r\u2013Rao bound","score":0.777652382850647},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5820331573486328},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.5076991319656372},{"id":"https://openalex.org/keywords/cardinal-point","display_name":"Cardinal point","score":0.47364556789398193},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.43296223878860474},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4302539527416229},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4277898073196411},{"id":"https://openalex.org/keywords/point-spread-function","display_name":"Point spread function","score":0.41877421736717224},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.38535478711128235},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38463109731674194},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3622766435146332},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.32155299186706543},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2886970043182373},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.16267457604408264}],"concepts":[{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.8558254241943359},{"id":"https://openalex.org/C4978587","wikidata":"https://www.wikidata.org/wiki/Q1138810","display_name":"Cram\u00e9r\u2013Rao bound","level":3,"score":0.777652382850647},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5820331573486328},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.5076991319656372},{"id":"https://openalex.org/C138395690","wikidata":"https://www.wikidata.org/wiki/Q376733","display_name":"Cardinal point","level":2,"score":0.47364556789398193},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.43296223878860474},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4302539527416229},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4277898073196411},{"id":"https://openalex.org/C69179731","wikidata":"https://www.wikidata.org/wiki/Q510427","display_name":"Point spread function","level":2,"score":0.41877421736717224},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.38535478711128235},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38463109731674194},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3622766435146332},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.32155299186706543},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2886970043182373},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.16267457604408264},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"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/taes.2020.3018900","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taes.2020.3018900","pdf_url":null,"source":{"id":"https://openalex.org/S193624734","display_name":"IEEE Transactions on Aerospace and Electronic Systems","issn_l":"0018-9251","issn":["0018-9251","1557-9603","2371-9877"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Aerospace and Electronic Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.8100000023841858}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W248985578","https://openalex.org/W1531532259","https://openalex.org/W1583517565","https://openalex.org/W1590583843","https://openalex.org/W1965392255","https://openalex.org/W1967071527","https://openalex.org/W2041684917","https://openalex.org/W2109042903","https://openalex.org/W2286618566","https://openalex.org/W2624718528","https://openalex.org/W2744044201","https://openalex.org/W2801132354","https://openalex.org/W2912546297","https://openalex.org/W2944238747","https://openalex.org/W4250589301","https://openalex.org/W4251448448","https://openalex.org/W4388900512","https://openalex.org/W6609441573","https://openalex.org/W6684320684"],"related_works":["https://openalex.org/W2349645049","https://openalex.org/W2077678701","https://openalex.org/W2139969424","https://openalex.org/W2991328497","https://openalex.org/W2378149154","https://openalex.org/W2801923456","https://openalex.org/W3020479456","https://openalex.org/W3080142519","https://openalex.org/W1997351648","https://openalex.org/W2156178937"],"abstract_inverted_index":{"This":[0],"article":[1],"extends":[2],"previous":[3],"work":[4,21,37],"on":[5,271],"location":[6],"and":[7,29,46,87,204,217,229,252,267],"intensity":[8],"estimation":[9,121,165],"for":[10,82,123],"measurement":[11],"extraction":[12,84],"of":[13,32,98,106,119,166,176,191,208,243,263],"targets":[14,28,31,43,132,147,193],"in":[15,188,197,205],"a":[16,52,77,99,173,219,225,231],"focal":[17],"plane":[18],"array.":[19],"Prior":[20],"has":[22],"been":[23],"done":[24],"to":[25,71,201,212,236,255],"extract":[26],"single":[27,100],"two":[30,42,108,192],"equal":[33],"intensity,":[34],"whereas":[35],"this":[36,244],"explores":[38],"the":[39,91,96,104,107,114,120,140,143,146,167,189,195,198,206,209,238,261],"case":[40,207],"where":[41],"have":[44,133],"unequal":[45],"unknown":[47],"intensities.":[48],"Here,":[49],"we":[50,94,183,259],"assume":[51],"Gaussian":[53],"point":[54],"spread":[55,59],"function":[56],"(PSF)":[57],"with":[58],"\u03c3":[60],"<sub":[61,152,178],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[62,153,179],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">PSF</sub>":[63,154,180],",":[64,155],"but":[65],"our":[66],"approach":[67],"could":[68],"be":[69,202],"applied":[70],"other":[72],"PSF":[73],"shapes.":[74],"We":[75,111,161,214],"present":[76,113],"maximum":[78],"likelihood":[79,233],"(ML)":[80],"method":[81],"target":[83,101,174,222,269],"under":[85],"resolved":[86,131,199],"unresolved":[88,92,109,210],"assumptions.":[89],"In":[90],"case,":[93],"estimate":[95],"parameters":[97,169],"that":[102,130,164,185],"represents":[103],"centroid":[105,168],"targets.":[110],"also":[112,162,215],"Cramer-Rao":[115],"lower":[116],"bound":[117],"(CRLB)":[118],"variances":[122,196],"both":[124],"cases.":[125],"Our":[126],"simulation":[127],"results":[128],"show":[129],"their":[134],"parameter":[135],"vectors":[136],"estimated":[137],"efficiently":[138],"(i.e.,":[139],"variance":[141],"meets":[142],"CRLB)":[144],"when":[145],"are":[148],"separated":[149],"by":[150],"0.9\u03c3":[151],"or":[156],"about":[157,221],"1.8":[158],"pixel":[159],"widths.":[160],"find":[163,184],"is":[170,246],"efficient":[171],"below":[172],"separation":[175,264],"0.65\u03c3":[177],".":[181],"Furthermore,":[182],"increased":[186],"difference":[187],"SNR":[190,270],"causes":[194],"scenario":[200],"lower,":[203],"scenario,":[211],"increase.":[213],"derive":[216],"characterize":[218],"decision":[220,239],"cardinality":[223],"as":[224],"hypothesis":[226],"testing":[227],"problem,":[228],"develop":[230],"generalized":[232],"ratio":[234],"test":[235,245],"perform":[237],"making.":[240],"The":[241],"performance":[242],"evaluated":[247],"via":[248],"Monte":[249],"Carlo":[250],"simulations,":[251],"matches":[253],"well":[254],"theoretical":[256],"predictions.":[257],"Finally,":[258],"explore":[260],"effect":[262],"between":[265],"targets,":[266],"individual":[268],"resolvability.":[272]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}