{"id":"https://openalex.org/W1598520546","doi":"https://doi.org/10.1109/lsp.2015.2464154","title":"Sequential Bayesian Algorithms for Identification and Blind Equalization of Unit-Norm Channels","display_name":"Sequential Bayesian Algorithms for Identification and Blind Equalization of Unit-Norm Channels","publication_year":2015,"publication_date":"2015-08-03","ids":{"openalex":"https://openalex.org/W1598520546","doi":"https://doi.org/10.1109/lsp.2015.2464154","mag":"1598520546"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2015.2464154","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2015.2464154","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","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/A5066331505","display_name":"Claudio J. Bordin","orcid":"https://orcid.org/0000-0002-7016-5922"},"institutions":[{"id":"https://openalex.org/I71715416","display_name":"Universidade Federal do ABC","ror":"https://ror.org/028kg9j04","country_code":"BR","type":"education","lineage":["https://openalex.org/I71715416"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Claudio J. Bordin","raw_affiliation_strings":["Santo Andr\u00e9, Universidade Federal do ABC, Brazil","Universidade Federal do ABC, Santo Andr\u00e9, Brazil#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Santo Andr\u00e9, Universidade Federal do ABC, Brazil","institution_ids":["https://openalex.org/I71715416"]},{"raw_affiliation_string":"Universidade Federal do ABC, Santo Andr\u00e9, Brazil#TAB#","institution_ids":["https://openalex.org/I71715416"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018888863","display_name":"Marcelo G. S. Bruno","orcid":"https://orcid.org/0000-0003-2269-4018"},"institutions":[{"id":"https://openalex.org/I107428990","display_name":"Instituto Tecnol\u00f3gico de Aeron\u00e1utica","ror":"https://ror.org/05vh67662","country_code":"BR","type":"education","lineage":["https://openalex.org/I107428990"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Marcelo G. S. Bruno","raw_affiliation_strings":["S\u00e3o Jos\u00e9 dos Campos, Instituto Tecnol\u00f3gico de Aeron\u00e1utica, Brazil","Instituto Tecnol\u00f3gico de Aeron\u00e1utica S\u00e3o Jos\u00e9 dos Campos Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"S\u00e3o Jos\u00e9 dos Campos, Instituto Tecnol\u00f3gico de Aeron\u00e1utica, Brazil","institution_ids":["https://openalex.org/I107428990"]},{"raw_affiliation_string":"Instituto Tecnol\u00f3gico de Aeron\u00e1utica S\u00e3o Jos\u00e9 dos Campos Brazil","institution_ids":["https://openalex.org/I107428990"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7807,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.88178892,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"22","issue":"11","first_page":"2157","last_page":"2161"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9993000030517578,"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"}},"topics":[{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9993000030517578,"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"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.998199999332428,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9954000115394592,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6624431610107422},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6503106951713562},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6081939339637756},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.5833998918533325},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.5634590983390808},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5633218884468079},{"id":"https://openalex.org/keywords/blind-equalization","display_name":"Blind equalization","score":0.5558009147644043},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4913109838962555},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.47947487235069275},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.47263190150260925},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.46088242530822754},{"id":"https://openalex.org/keywords/conjugate-prior","display_name":"Conjugate prior","score":0.4439772963523865},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.4368418753147125},{"id":"https://openalex.org/keywords/system-identification","display_name":"System identification","score":0.42716872692108154},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.41945263743400574},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3949263095855713},{"id":"https://openalex.org/keywords/equalization","display_name":"Equalization (audio)","score":0.29536569118499756},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.2896394729614258},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18075621128082275},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.14912930130958557},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.1160484254360199},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.10591199994087219}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6624431610107422},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6503106951713562},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6081939339637756},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.5833998918533325},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.5634590983390808},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5633218884468079},{"id":"https://openalex.org/C41425797","wikidata":"https://www.wikidata.org/wiki/Q4926640","display_name":"Blind equalization","level":4,"score":0.5558009147644043},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4913109838962555},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.47947487235069275},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.47263190150260925},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.46088242530822754},{"id":"https://openalex.org/C26004113","wikidata":"https://www.wikidata.org/wiki/Q3711784","display_name":"Conjugate prior","level":4,"score":0.4439772963523865},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.4368418753147125},{"id":"https://openalex.org/C119247159","wikidata":"https://www.wikidata.org/wiki/Q1366192","display_name":"System identification","level":3,"score":0.42716872692108154},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41945263743400574},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3949263095855713},{"id":"https://openalex.org/C75755367","wikidata":"https://www.wikidata.org/wiki/Q104531076","display_name":"Equalization (audio)","level":3,"score":0.29536569118499756},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.2896394729614258},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18075621128082275},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.14912930130958557},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.1160484254360199},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.10591199994087219},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/lsp.2015.2464154","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2015.2464154","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1483307070","https://openalex.org/W1745126362","https://openalex.org/W1776966358","https://openalex.org/W1963545391","https://openalex.org/W1975697837","https://openalex.org/W1978092817","https://openalex.org/W2018345399","https://openalex.org/W2050386482","https://openalex.org/W2062130737","https://openalex.org/W2085738358","https://openalex.org/W2098630413","https://openalex.org/W2099335010","https://openalex.org/W2105906326","https://openalex.org/W2107201697","https://openalex.org/W2110338450","https://openalex.org/W2113388284","https://openalex.org/W2119434824","https://openalex.org/W2121202969","https://openalex.org/W2123259786","https://openalex.org/W2126736494","https://openalex.org/W2128198741","https://openalex.org/W2128234918","https://openalex.org/W2130363011","https://openalex.org/W2136111578","https://openalex.org/W2136532252","https://openalex.org/W2144421450","https://openalex.org/W2145243777","https://openalex.org/W2145670799","https://openalex.org/W2148579177","https://openalex.org/W2151481655","https://openalex.org/W2155061848","https://openalex.org/W2160337655","https://openalex.org/W2277299577","https://openalex.org/W2470235696","https://openalex.org/W2496244789","https://openalex.org/W2610805269","https://openalex.org/W2798333393","https://openalex.org/W3023287005","https://openalex.org/W4211126154","https://openalex.org/W4241181106","https://openalex.org/W4245808720","https://openalex.org/W4246202668","https://openalex.org/W4250150126","https://openalex.org/W6637748465"],"related_works":["https://openalex.org/W4298870584","https://openalex.org/W2734174941","https://openalex.org/W4306291977","https://openalex.org/W2950950409","https://openalex.org/W2232589144","https://openalex.org/W2085834335","https://openalex.org/W4298441262","https://openalex.org/W2404382874","https://openalex.org/W2905505054","https://openalex.org/W4287394650"],"abstract_inverted_index":{"In":[0,34],"many":[1],"estimation":[2],"problems":[3,29],"of":[4],"interest,":[5],"the":[6,43,57,61,66,83,86,129],"unknown":[7,44],"parameters":[8,20,62],"reside":[9],"on":[10,85,117],"spherical":[11],"manifolds.":[12],"As":[13,121],"most":[14],"common":[15],"filtering":[16],"algorithms":[17,139],"assume":[18],"that":[19,55,73,78,140],"have":[21,48],"Gaussian":[22,64,143],"prior":[23,51,144],"distributions,":[24],"their":[25],"application":[26],"to":[27,31,60,92,133,137],"such":[28],"leads":[30,132],"suboptimal":[32],"performance.":[33],"this":[35,90],"letter,":[36],"we":[37,122],"propose":[38],"a":[39,70,93,103,106,113],"model":[40,72,91,131],"in":[41],"which":[42],"unit-norm":[45],"parameter":[46,110],"vectors":[47],"Fisher-Bingham":[49],"(F-B)":[50],"distributions.":[52],"We":[53,88],"show":[54],"if":[56],"observations":[58],"relate":[59],"via":[63,124],"likelihoods,":[65],"F-B":[67,130],"priors":[68],"form":[69],"conjugate":[71],"yields":[74],"closed-form,":[75],"recursive":[76],"estimators":[77],"naturally":[79],"take":[80],"into":[81],"account":[82],"restrictions":[84],"unknowns.":[87],"apply":[89],"communication":[94],"setup":[95],"with":[96],"multiple":[97],"gain-controlled":[98],"FIR":[99],"frequency-selective":[100],"channels,":[101],"deriving":[102],"novel":[104],"maximum":[105],"posteriori":[107],"(MAP)":[108],"channel":[109],"estimator":[111],"and":[112],"blind":[114],"equalizer":[115],"based":[116],"Rao-Blackwellized":[118],"particle":[119],"filters.":[120],"verify":[123],"Monte":[125],"Carlo":[126],"numerical":[127],"simulations,":[128],"superior":[134],"performance":[135],"compared":[136],"previous":[138],"adopt":[141],"mismatched":[142],"models.":[145]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}