{"id":"https://openalex.org/W2889069284","doi":"https://doi.org/10.1109/ssp.2018.8450691","title":"A Non-Convex Approach To Joint Sensor Calibration And Spectrum Estimation","display_name":"A Non-Convex Approach To Joint Sensor Calibration And Spectrum Estimation","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2889069284","doi":"https://doi.org/10.1109/ssp.2018.8450691","mag":"2889069284"},"language":"en","primary_location":{"id":"doi:10.1109/ssp.2018.8450691","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2018.8450691","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","raw_type":"proceedings-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/A5103181623","display_name":"Myung Cho","orcid":"https://orcid.org/0000-0002-2383-3481"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Myung Cho","raw_affiliation_strings":["Department of ECE, Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Department of ECE, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050616963","display_name":"Wenjing Liao","orcid":"https://orcid.org/0000-0003-2309-3839"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenjing Liao","raw_affiliation_strings":["Department of Mathematics, Georgia Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053809095","display_name":"Yuejie Chi","orcid":"https://orcid.org/0000-0002-6766-5459"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuejie Chi","raw_affiliation_strings":["Department of ECE, Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Department of ECE, Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103181623"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":0.6606,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.690844,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"15","issue":null,"first_page":"398","last_page":"402"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9995999932289124,"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/T10860","display_name":"Speech and Audio Processing","score":0.9983999729156494,"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/covariance-matrix","display_name":"Covariance matrix","score":0.5737266540527344},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.5731117725372314},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.554737389087677},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.4996190071105957},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4750904142856598},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.47340521216392517},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.47234001755714417},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4623843729496002},{"id":"https://openalex.org/keywords/toeplitz-matrix","display_name":"Toeplitz matrix","score":0.4509049952030182},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.44752442836761475},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4018312990665436},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.35028380155563354},{"id":"https://openalex.org/keywords/control-theory","display_name":"Control theory (sociology)","score":0.3437207043170929},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13390719890594482},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.10800954699516296}],"concepts":[{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5737266540527344},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.5731117725372314},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.554737389087677},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.4996190071105957},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4750904142856598},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.47340521216392517},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.47234001755714417},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4623843729496002},{"id":"https://openalex.org/C147710293","wikidata":"https://www.wikidata.org/wiki/Q849428","display_name":"Toeplitz matrix","level":2,"score":0.4509049952030182},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.44752442836761475},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4018312990665436},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.35028380155563354},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.3437207043170929},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13390719890594482},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.10800954699516296},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.0},{"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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssp.2018.8450691","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2018.8450691","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","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":24,"referenced_works":["https://openalex.org/W589200591","https://openalex.org/W658204242","https://openalex.org/W1541064563","https://openalex.org/W1792675773","https://openalex.org/W1804110266","https://openalex.org/W2041507040","https://openalex.org/W2099418239","https://openalex.org/W2110662303","https://openalex.org/W2113638573","https://openalex.org/W2128131274","https://openalex.org/W2140867429","https://openalex.org/W2167623372","https://openalex.org/W2258202344","https://openalex.org/W2516015953","https://openalex.org/W2575581645","https://openalex.org/W2598194391","https://openalex.org/W2611469656","https://openalex.org/W2753305483","https://openalex.org/W2963380138","https://openalex.org/W3013302814","https://openalex.org/W4205293427","https://openalex.org/W6684641359","https://openalex.org/W6732473732","https://openalex.org/W6740737571"],"related_works":["https://openalex.org/W2353873167","https://openalex.org/W2170667396","https://openalex.org/W2756132392","https://openalex.org/W3035814349","https://openalex.org/W4285101096","https://openalex.org/W4382725876","https://openalex.org/W2084892497","https://openalex.org/W2115614142","https://openalex.org/W4320477335","https://openalex.org/W1561889708"],"abstract_inverted_index":{"Blind":[0],"sensor":[1,13,65,79],"calibration":[2,14,45],"for":[3,48],"spectrum":[4],"estimation":[5,50],"is":[6,66,85,107],"the":[7,11,19,22,77,92,96,100,117,139,142,173],"problem":[8,47,84,119,123],"of":[9,21,28,34,54,61,99,141],"estimating":[10],"unknown":[12,70,78],"parameters":[15],"as":[16,18],"well":[17],"parameters-of-interest":[20],"impinging":[23],"signals":[24],"simultaneously":[25],"from":[26,31,57],"snapshots":[27,53],"measurements":[29,55,101],"obtained":[30,56],"an":[32,58,69],"array":[33,60,106],"sensors.":[35],"In":[36],"this":[37,112],"paper,":[38],"we":[39,114],"consider":[40],"blind":[41],"phase":[42,73],"and":[43,72,80,130],"gain":[44,71],"(BPGC)":[46],"direction-of-arrival":[49],"with":[51],"multiple":[52],"uniform":[59],"sensors,":[62],"where":[63],"each":[64],"perturbed":[67],"by":[68],"parameter.":[74],"Due":[75],"to":[76,120,137,150],"signal":[81],"parameters,":[82],"BPGC":[83],"a":[86,103,108,121,133,146],"highly":[87],"nonlinear":[88,118],"problem.":[89],"Assuming":[90],"that":[91,157],"sources":[93],"are":[94],"uncorrelated,":[95],"covariance":[97],"matrix":[98,144],"in":[102,172],"perfectly":[104],"calibrated":[105],"Toeplitz":[109],"matrix.":[110],"Leveraging":[111],"fact,":[113],"first":[115],"change":[116],"linear":[122],"considering":[124],"certain":[125],"rank-one":[126,143],"positive":[127],"semidefinite":[128],"matrix,":[129],"then":[131],"suggest":[132],"non-convex":[134,160],"optimization":[135,161],"approach":[136,162],"find":[138],"factor":[140],"under":[145],"unit":[147],"norm":[148],"constraint":[149],"avoid":[151],"trivial":[152],"solutions.":[153],"Numerical":[154],"experiments":[155],"demonstrate":[156],"our":[158],"proposed":[159],"provides":[163],"better":[164],"or":[165],"competitive":[166],"recovery":[167],"performance":[168],"than":[169],"existing":[170],"methods":[171],"literature,":[174],"without":[175],"requiring":[176],"any":[177],"tuning":[178],"parameters.":[179]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
