{"id":"https://openalex.org/W7125949496","doi":"https://doi.org/10.1109/smc58881.2025.11343094","title":"A Variational Bayesian Framework for Simultaneous Input and State Estimation","display_name":"A Variational Bayesian Framework for Simultaneous Input and State Estimation","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125949496","doi":"https://doi.org/10.1109/smc58881.2025.11343094"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11343094","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343094","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5107267391","display_name":"Kunpeng Ren","orcid":null},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kunpeng Ren","raw_affiliation_strings":["Southwest University,School of Computer and Information Science,Chongqing,China,400715"],"affiliations":[{"raw_affiliation_string":"Southwest University,School of Computer and Information Science,Chongqing,China,400715","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103433282","display_name":"Z Q Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zihan Xu","raw_affiliation_strings":["Southwest University,School of Computer and Information Science,Chongqing,China,400715"],"affiliations":[{"raw_affiliation_string":"Southwest University,School of Computer and Information Science,Chongqing,China,400715","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100375979","display_name":"Yuxuan Wang","orcid":"https://orcid.org/0000-0002-1649-6974"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yizhen Wang","raw_affiliation_strings":["Southwest University,School of Computer and Information Science,Chongqing,China,400715"],"affiliations":[{"raw_affiliation_string":"Southwest University,School of Computer and Information Science,Chongqing,China,400715","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123848303","display_name":"Le Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Le Yin","raw_affiliation_strings":["Southwest University,School of Computer and Information Science,Chongqing,China,400715"],"affiliations":[{"raw_affiliation_string":"Southwest University,School of Computer and Information Science,Chongqing,China,400715","institution_ids":["https://openalex.org/I142108993"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5107267391"],"corresponding_institution_ids":["https://openalex.org/I142108993"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.83932145,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4984","last_page":"4989"},"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":0.6384000182151794,"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":0.6384000182151794,"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":0.06040000170469284,"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/T11236","display_name":"Control Systems and Identification","score":0.0471000000834465,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5766000151634216},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5515999794006348},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.5210999846458435},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.4871000051498413},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4142000079154968},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.3959999978542328},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.3797999918460846},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.3637000024318695},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.3407000005245209}],"concepts":[{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5766000151634216},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5515999794006348},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5300999879837036},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.5210999846458435},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.4871000051498413},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4512999951839447},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44600000977516174},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4406999945640564},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4142000079154968},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.3959999978542328},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.3797999918460846},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3637000024318695},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.3637000024318695},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.3407000005245209},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.3400999903678894},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.31459999084472656},{"id":"https://openalex.org/C33962027","wikidata":"https://www.wikidata.org/wiki/Q1930697","display_name":"Wishart distribution","level":3,"score":0.31369999051094055},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.30709999799728394},{"id":"https://openalex.org/C40343088","wikidata":"https://www.wikidata.org/wiki/Q3059012","display_name":"Recursive Bayesian estimation","level":3,"score":0.2996000051498413},{"id":"https://openalex.org/C129537906","wikidata":"https://www.wikidata.org/wiki/Q7603913","display_name":"State variable","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.29010000824928284},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.28130000829696655},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11343094","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343094","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1531532259","https://openalex.org/W1970227820","https://openalex.org/W2000721204","https://openalex.org/W2016026577","https://openalex.org/W2020735846","https://openalex.org/W2030917988","https://openalex.org/W2038889147","https://openalex.org/W2049789198","https://openalex.org/W2057288362","https://openalex.org/W2057798714","https://openalex.org/W2064815138","https://openalex.org/W2088735810","https://openalex.org/W2094564909","https://openalex.org/W2095376999","https://openalex.org/W2120347155","https://openalex.org/W2125079402","https://openalex.org/W2142089368","https://openalex.org/W2143030612","https://openalex.org/W2148796727","https://openalex.org/W2150449423","https://openalex.org/W2461017019","https://openalex.org/W2575097171","https://openalex.org/W2623904884","https://openalex.org/W2737188128","https://openalex.org/W2749496335","https://openalex.org/W2944846513","https://openalex.org/W2996562830","https://openalex.org/W3133243397","https://openalex.org/W3217524693","https://openalex.org/W4383097042","https://openalex.org/W4401329406","https://openalex.org/W4402508732","https://openalex.org/W4408345992","https://openalex.org/W4412352961"],"related_works":[],"abstract_inverted_index":{"In":[0,16],"this":[1],"paper,":[2],"a":[3],"variational":[4,72],"Bayesian":[5,73],"framework":[6],"for":[7],"simultaneous":[8],"input":[9,33,46,80,100,123],"and":[10,54,58,68,81,124],"state":[11,82,125],"estimation":[12,126],"(VBSISE)":[13],"is":[14,66],"proposed.":[15],"VBSISE,":[17],"probability":[18],"density":[19],"functions":[20],"(PDFs)":[21],"are":[22,50,84],"assigned":[23],"to":[24,31,75,99],"inputs,":[25],"with":[26],"their":[27],"parameters":[28],"adaptively":[29],"updated":[30],"enhance":[32],"estimation.":[34],"This":[35],"offers":[36],"greater":[37],"flexibility":[38],"than":[39],"existing":[40,119],"methods":[41,104],"that":[42,105,114],"only":[43],"handle":[44],"fixed":[45,108],"conditions.":[47],"Specifically,":[48],"inputs":[49],"modeled":[51],"using":[52],"Gaussian":[53],"inverse":[55],"Wishart":[56],"distributions,":[57],"the":[59,63,71,88,91,115],"joint":[60],"PDF":[61],"of":[62,90],"estimated":[64],"variables":[65],"derived":[67],"approximated":[69,92],"via":[70],"approach":[74],"obtain":[76],"tractable":[77],"distributions.":[78],"The":[79,94],"estimates":[83],"then":[85],"obtained":[86],"as":[87],"means":[89],"PDFs.":[93],"proposed":[95,116],"VBSISE":[96,117],"effectively":[97],"adapts":[98],"variations,":[101],"unlike":[102],"conventional":[103],"rely":[106],"on":[107],"statistical":[109],"moments.":[110],"Experimental":[111],"results":[112],"demonstrate":[113],"outperforms":[118],"filters":[120],"in":[121],"both":[122],"accuracy.":[127]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-29T00:00:00"}
