{"id":"https://openalex.org/W2005662236","doi":"https://doi.org/10.1109/spawc.2013.6612084","title":"On the performance of joint linear minimum mean squared error (LMMSE) filtering and parameter estimation","display_name":"On the performance of joint linear minimum mean squared error (LMMSE) filtering and parameter estimation","publication_year":2013,"publication_date":"2013-06-01","ids":{"openalex":"https://openalex.org/W2005662236","doi":"https://doi.org/10.1109/spawc.2013.6612084","mag":"2005662236"},"language":"en","primary_location":{"id":"doi:10.1109/spawc.2013.6612084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spawc.2013.6612084","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","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/A5017099088","display_name":"Siouar Bensaid","orcid":null},"institutions":[{"id":"https://openalex.org/I1902872","display_name":"EURECOM","ror":"https://ror.org/00sse7z02","country_code":"FR","type":"education","lineage":["https://openalex.org/I1902872","https://openalex.org/I205703379"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Siouar Bensaid","raw_affiliation_strings":["Mobile Communications Department, EURECOM, France","Mobile Communication Department EURECOM Sophia Antipolis, France"],"affiliations":[{"raw_affiliation_string":"Mobile Communications Department, EURECOM, France","institution_ids":["https://openalex.org/I1902872"]},{"raw_affiliation_string":"Mobile Communication Department EURECOM Sophia Antipolis, France","institution_ids":["https://openalex.org/I1902872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014663629","display_name":"Dirk Slock","orcid":"https://orcid.org/0000-0003-4116-563X"},"institutions":[{"id":"https://openalex.org/I1902872","display_name":"EURECOM","ror":"https://ror.org/00sse7z02","country_code":"FR","type":"education","lineage":["https://openalex.org/I1902872","https://openalex.org/I205703379"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Dirk Slock","raw_affiliation_strings":["Mobile Communications Department, EURECOM, France","Mobile Communication Department EURECOM Sophia Antipolis, France"],"affiliations":[{"raw_affiliation_string":"Mobile Communications Department, EURECOM, France","institution_ids":["https://openalex.org/I1902872"]},{"raw_affiliation_string":"Mobile Communication Department EURECOM Sophia Antipolis, France","institution_ids":["https://openalex.org/I1902872"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5017099088"],"corresponding_institution_ids":["https://openalex.org/I1902872"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08077256,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"35","issue":null,"first_page":"420","last_page":"424"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9994000196456909,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9994000196456909,"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/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9990000128746033,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.998199999332428,"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/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.7262659668922424},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.6718438863754272},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6408020257949829},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.5923956036567688},{"id":"https://openalex.org/keywords/minimum-mean-square-error","display_name":"Minimum mean square error","score":0.5838119983673096},{"id":"https://openalex.org/keywords/maximum-likelihood-sequence-estimation","display_name":"Maximum likelihood sequence estimation","score":0.5123022794723511},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.49247822165489197},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.4874861240386963},{"id":"https://openalex.org/keywords/cram\u00e9r\u2013rao-bound","display_name":"Cram\u00e9r\u2013Rao bound","score":0.44929495453834534},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.436550498008728},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4167206287384033},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.41532397270202637},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3980135917663574},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.393466055393219},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.25234395265579224},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.16744068264961243}],"concepts":[{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.7262659668922424},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.6718438863754272},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6408020257949829},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.5923956036567688},{"id":"https://openalex.org/C90652560","wikidata":"https://www.wikidata.org/wiki/Q11091747","display_name":"Minimum mean square error","level":3,"score":0.5838119983673096},{"id":"https://openalex.org/C191462741","wikidata":"https://www.wikidata.org/wiki/Q6795902","display_name":"Maximum likelihood sequence estimation","level":3,"score":0.5123022794723511},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.49247822165489197},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.4874861240386963},{"id":"https://openalex.org/C4978587","wikidata":"https://www.wikidata.org/wiki/Q1138810","display_name":"Cram\u00e9r\u2013Rao bound","level":3,"score":0.44929495453834534},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.436550498008728},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4167206287384033},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.41532397270202637},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3980135917663574},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.393466055393219},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.25234395265579224},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.16744068264961243},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/spawc.2013.6612084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spawc.2013.6612084","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","raw_type":"proceedings-article"},{"id":"pmh:oai:fr.eurecom:4054","is_oa":false,"landing_page_url":"http://www.eurecom.fr/publication/4054","pdf_url":null,"source":{"id":"https://openalex.org/S4377196942","display_name":"Graduate School and Research Center in Digital Science (EURECOM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1902872","host_organization_name":"EURECOM","host_organization_lineage":["https://openalex.org/I1902872"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"SPAWC 2013, 14th IEEE International Workshop on Signal Processing Advances in Wireless Communications, June 16-19, 2013, Darmstadt, Germany ","raw_type":"Conference"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.75,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1536252645","https://openalex.org/W1676523307","https://openalex.org/W1991934991","https://openalex.org/W1998450217","https://openalex.org/W2019625237","https://openalex.org/W2027780595","https://openalex.org/W2034767804","https://openalex.org/W2037973502","https://openalex.org/W2049633694","https://openalex.org/W2077305791","https://openalex.org/W2083592286","https://openalex.org/W2100841352","https://openalex.org/W2107189492","https://openalex.org/W2108667507","https://openalex.org/W2111540686","https://openalex.org/W2115979064","https://openalex.org/W2117413897","https://openalex.org/W2120717581","https://openalex.org/W2120746275","https://openalex.org/W2122512809","https://openalex.org/W2122555105","https://openalex.org/W2127766747","https://openalex.org/W2133301970","https://openalex.org/W2149032657","https://openalex.org/W2154191559","https://openalex.org/W2156077606","https://openalex.org/W2169313784","https://openalex.org/W2565753657","https://openalex.org/W4246841922","https://openalex.org/W7048738093"],"related_works":["https://openalex.org/W2022738044","https://openalex.org/W1967494390","https://openalex.org/W1783992599","https://openalex.org/W2114899076","https://openalex.org/W2045588782","https://openalex.org/W2124697778","https://openalex.org/W1503532423","https://openalex.org/W2135468550","https://openalex.org/W2133422797","https://openalex.org/W2113767069"],"abstract_inverted_index":{"We":[0,120,165],"consider":[1,105],"the":[2,14,23,51,62,68,81,85,92,122,125,132,137,142,151,222],"problem":[3,39,48],"of":[4,16,19,84,95,157,190],"LMMSE":[5],"estimation":[6,38,47,79,173],"(such":[7],"as":[8],"Wiener":[9],"and":[10,36,61,109,111,131,140,150,181,211],"Kalman":[11],"filtering)":[12],"in":[13,22,49,188],"presence":[15],"a":[17,45,162,191],"number":[18],"unknown":[20,63],"parameters":[21,64,185],"second-order":[24],"statistics,":[25],"that":[26],"need":[27],"to":[28,53,116,203],"be":[29,54,75],"estimated":[30,55],"also.":[31],"This":[32],"well-known":[33],"joint":[34,96,172],"filtering":[35,58],"parameter":[37,78,99,197,223],"has":[40],"numerous":[41],"applications.":[42],"It":[43],"is":[44,59,70,90,161],"hybrid":[46],"which":[50,160,187,201,215],"signal":[52,69,108,209,219],"by":[56,148],"linear":[57],"random,":[60,71],"are":[65],"deterministic.":[66],"As":[67],"it":[72],"can":[73],"also":[74],"eliminated,":[76],"allowing":[77],"from":[80,195],"marginal":[82],"distribution":[83],"data.":[86],"An":[87],"intriguing":[88],"question":[89],"then":[91,166],"relative":[93],"performance":[94],"vs.":[97],"marginalized":[98,134],"estimation.":[100],"In":[101],"this":[102],"paper,":[103],"we":[104,112],"jointly":[106],"Gaussian":[107],"data":[110],"first":[113],"provide":[114],"contributions":[115],"Cramer-Rao":[117],"bounds":[118],"(CRBs).":[119],"characterize":[121],"difference":[123],"between":[124,141],"Hybrid":[126],"Fisher":[127],"Information":[128],"Matrix":[129],"(HFIM)":[130],"classical":[133],"FIM":[135,143,154],"on":[136],"one":[138],"hand,":[139],"(with":[144],"CRB":[145],"asymptotically":[146,225],"attained":[147],"ML)":[149],"popular":[152],"Modified":[153,158],"(MFIM,":[155],"inverse":[156],"CRB)":[159],"loose":[163],"bound.":[164],"investigate":[167],"three":[168],"iterative":[169],"(alternating":[170],"optimization)":[171],"approaches:":[174],"Alternating":[175],"Maximum":[176,182],"A":[177],"Posteriori":[178],"for":[179,184],"Signal":[180],"Likelihood":[183],"(AMAPML),":[186],"spite":[189],"better":[192],"HFIM":[193],"suffers":[194],"inconsistent":[196],"bias,":[198],"Expectation-Maximization":[199],"(EM)":[200],"converges":[202],"(marginalized)":[204],"ML":[205],"(but":[206],"with":[207,221],"AMAPML":[208],"estimate),":[210],"Variational":[212],"Bayes":[213],"(VB)":[214],"yields":[216],"an":[217],"improved":[218],"estimate":[220,224],"becoming":[226],"ML.":[227]},"counts_by_year":[],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}