{"id":"https://openalex.org/W4415524466","doi":"https://doi.org/10.1109/mlsp62443.2025.11204271","title":"Coordinate Ascent Neural Kalman-MLE for State Estimation","display_name":"Coordinate Ascent Neural Kalman-MLE for State Estimation","publication_year":2025,"publication_date":"2025-08-31","ids":{"openalex":"https://openalex.org/W4415524466","doi":"https://doi.org/10.1109/mlsp62443.2025.11204271"},"language":null,"primary_location":{"id":"doi:10.1109/mlsp62443.2025.11204271","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp62443.2025.11204271","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 35th International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5111359667","display_name":"Bettina Hanlon","orcid":null},"institutions":[{"id":"https://openalex.org/I146655781","display_name":"University of Liverpool","ror":"https://ror.org/04xs57h96","country_code":"GB","type":"education","lineage":["https://openalex.org/I146655781"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Bettina Hanlon","raw_affiliation_strings":["University of Liverpool,Department of Electrical Engineering and Electronics,UK"],"affiliations":[{"raw_affiliation_string":"University of Liverpool,Department of Electrical Engineering and Electronics,UK","institution_ids":["https://openalex.org/I146655781"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049418113","display_name":"\u00c1ngel F. Garc\u00eda\u2010Fern\u00e1ndez","orcid":"https://orcid.org/0000-0002-6471-8455"},"institutions":[{"id":"https://openalex.org/I6585545","display_name":"European Telecommunications Standards Institute","ror":"https://ror.org/00pwny267","country_code":"FR","type":"nonprofit","lineage":["https://openalex.org/I6585545"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"\u00c1ngel F. Garc\u00eda-Fern\u00e1ndez","raw_affiliation_strings":["Universidad Politécnica de Madrid,IPTC, ETSI de Telecomunicación,Spain"],"affiliations":[{"raw_affiliation_string":"Universidad Politécnica de Madrid,IPTC, ETSI de Telecomunicación,Spain","institution_ids":["https://openalex.org/I6585545"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5111359667"],"corresponding_institution_ids":["https://openalex.org/I146655781"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17005094,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9865999817848206,"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/T10320","display_name":"Neural Networks and Applications","score":0.9865999817848206,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9585000276565552,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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":0.9490000009536743,"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/kalman-filter","display_name":"Kalman filter","score":0.6801999807357788},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5329999923706055},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5246999859809875},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5146999955177307},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.47699999809265137},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4693000018596649},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.4440000057220459},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.43549999594688416},{"id":"https://openalex.org/keywords/optimal-estimation","display_name":"Optimal estimation","score":0.39239999651908875}],"concepts":[{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.6801999807357788},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5329999923706055},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5264000296592712},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5246999859809875},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5146999955177307},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.47699999809265137},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4693000018596649},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.4440000057220459},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.43549999594688416},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4341000020503998},{"id":"https://openalex.org/C39425265","wikidata":"https://www.wikidata.org/wiki/Q7098944","display_name":"Optimal estimation","level":2,"score":0.39239999651908875},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.38100001215934753},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.3765999972820282},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.3695000112056732},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.3691999912261963},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3476000130176544},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.34290000796318054},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3278999924659729},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3278000056743622},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32749998569488525},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.32710000872612},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.2809999883174896},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C2983030100","wikidata":"https://www.wikidata.org/wiki/Q638328","display_name":"Nonlinear dynamical systems","level":3,"score":0.27079999446868896},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C19619285","wikidata":"https://www.wikidata.org/wiki/Q196372","display_name":"Observational error","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C52918065","wikidata":"https://www.wikidata.org/wiki/Q230945","display_name":"State-space representation","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp62443.2025.11204271","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp62443.2025.11204271","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 35th International Workshop on Machine Learning for Signal Processing (MLSP)","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":15,"referenced_works":["https://openalex.org/W1808206046","https://openalex.org/W2016456873","https://openalex.org/W2098946742","https://openalex.org/W2119539043","https://openalex.org/W2123487311","https://openalex.org/W2143737806","https://openalex.org/W2708033136","https://openalex.org/W2979647272","https://openalex.org/W3183282730","https://openalex.org/W3200702630","https://openalex.org/W3201337335","https://openalex.org/W4226294020","https://openalex.org/W4388088341","https://openalex.org/W4392904628","https://openalex.org/W4403278284"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,22,68],"coordinate":[4],"ascent":[5],"algorithm":[6,39,72],"to":[7,34,73],"learn":[8],"dynamic":[9,14,28,48,60],"and":[10,29,37,49,52,61],"measurement":[11,30,50,62],"models":[12,31,63],"in":[13,21],"state":[15,76],"estimation":[16,20],"using":[17],"maximum":[18],"likelihood":[19],"supervised":[23],"manner.":[24],"In":[25],"particular,":[26],"the":[27,38,41,47,54,75,78],"are":[32,64],"assumed":[33],"be":[35],"Gaussian":[36],"learns":[40],"neural":[42],"network":[43],"parameters":[44],"that":[45],"model":[46],"functions,":[51],"also":[53],"noise":[55],"covariance":[56],"matrices.":[57],"The":[58],"trained":[59],"then":[65],"used":[66],"with":[67],"nonlinear":[69],"Kalman":[70],"filter":[71],"estimate":[74],"during":[77],"testing":[79],"phase.":[80]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-24T00:00:00"}