{"id":"https://openalex.org/W7134858622","doi":"https://doi.org/10.1109/jiot.2026.3672319","title":"Enhanced Deep-Learning-Aided Kalman Filter Based on KalmanNet for Robust Nonlinear State Estimation","display_name":"Enhanced Deep-Learning-Aided Kalman Filter Based on KalmanNet for Robust Nonlinear State Estimation","publication_year":2026,"publication_date":"2026-03-09","ids":{"openalex":"https://openalex.org/W7134858622","doi":"https://doi.org/10.1109/jiot.2026.3672319"},"language":null,"primary_location":{"id":"doi:10.1109/jiot.2026.3672319","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3672319","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","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/A5103572428","display_name":"Tianxuan Fu","orcid":"https://orcid.org/0009-0009-2933-0082"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianxuan Fu","raw_affiliation_strings":["Smart Transportation Institute and the School of Telecommunications Engineering, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0009-0009-2933-0082","affiliations":[{"raw_affiliation_string":"Smart Transportation Institute and the School of Telecommunications Engineering, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036000787","display_name":"Guoqiang Mao","orcid":"https://orcid.org/0000-0002-3598-4949"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoqiang Mao","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-3598-4949","affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063142464","display_name":"Zehua Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zehua Wang","raw_affiliation_strings":["Smart Transportation Institute and the School of Telecommunications Engineering, Xidian University, Xi’an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Smart Transportation Institute and the School of Telecommunications Engineering, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101313757","display_name":"Keyin Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Keyin Wang","raw_affiliation_strings":["Smart Transportation Institute and the School of Telecommunications Engineering, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0009-0002-6628-4600","affiliations":[{"raw_affiliation_string":"Smart Transportation Institute and the School of Telecommunications Engineering, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":30.6816,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.99528898,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"13","issue":"11","first_page":"24369","last_page":"24386"},"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":0.43290001153945923,"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":0.43290001153945923,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.06589999794960022,"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":0.05130000039935112,"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/kalman-filter","display_name":"Kalman filter","score":0.7432000041007996},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.6358000040054321},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5221999883651733},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.5162000060081482},{"id":"https://openalex.org/keywords/control-theory","display_name":"Control theory (sociology)","score":0.44859999418258667},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.43479999899864197},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4092000126838684},{"id":"https://openalex.org/keywords/invariant-extended-kalman-filter","display_name":"Invariant extended Kalman filter","score":0.3968999981880188},{"id":"https://openalex.org/keywords/fast-kalman-filter","display_name":"Fast Kalman filter","score":0.3912000060081482}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7703999876976013},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.7432000041007996},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.6358000040054321},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5221999883651733},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5162000060081482},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.44859999418258667},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.43479999899864197},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4239000082015991},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4092000126838684},{"id":"https://openalex.org/C8639503","wikidata":"https://www.wikidata.org/wiki/Q6059511","display_name":"Invariant extended Kalman filter","level":4,"score":0.3968999981880188},{"id":"https://openalex.org/C150679823","wikidata":"https://www.wikidata.org/wiki/Q5436946","display_name":"Fast Kalman filter","level":4,"score":0.3912000060081482},{"id":"https://openalex.org/C79334102","wikidata":"https://www.wikidata.org/wiki/Q3072268","display_name":"Ensemble Kalman filter","level":4,"score":0.38260000944137573},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.36010000109672546},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.3573000133037567},{"id":"https://openalex.org/C77405623","wikidata":"https://www.wikidata.org/wiki/Q598451","display_name":"System dynamics","level":2,"score":0.3528999984264374},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3212999999523163},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.30720001459121704},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3068000078201294},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C6802819","wikidata":"https://www.wikidata.org/wiki/Q1072174","display_name":"Linear system","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C50897621","wikidata":"https://www.wikidata.org/wiki/Q2665508","display_name":"Hybrid system","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.2515000104904175},{"id":"https://openalex.org/C137685913","wikidata":"https://www.wikidata.org/wiki/Q4316763","display_name":"Nonlinear filter","level":4,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2026.3672319","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3672319","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.656282901763916,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G8007125788","display_name":null,"funder_award_id":"U21A20446","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurately":[0],"estimating":[1],"the":[2,67,120,126,178,181,185,198,202,205,211,229,254],"real-time":[3],"state":[4,35,58,154],"of":[5,180,207,213],"dynamic":[6,117],"systems":[7],"from":[8,156],"noisy":[9],"measurements":[10],"is":[11,123,129,137,165],"a":[12,104,133,141,160],"fundamental":[13],"task":[14],"in":[15,32,115,247],"signal":[16],"processing.":[17],"State-space":[18],"models":[19,88],"(SSM)":[20],"are":[21,46,74,266],"commonly":[22],"utilized":[23,166],"to":[24,56,112,147,167],"model":[25,72,122,136,259],"system":[26,34,68,127],"dynamics":[27,69,151],"and":[28,37,43,54,60,70,86,94,125,152,210,232,244,257,264,276],"account":[29],"for":[30,49,77],"uncertainty":[31],"both":[33],"evolution":[36,59],"measurement":[38,61,87,135,173],"data.":[39],"Kalman":[40,109,182,186,215],"filter":[41,187],"(KF)":[42],"its":[44],"variants":[45],"widely":[47],"recognized":[48],"their":[50],"low":[51],"computational":[52],"complexity":[53],"ability":[55],"address":[57],"update.":[62],"In":[63,98],"practice,":[64],"accurately":[65],"modeling":[66],"obtaining":[71],"parameters":[73],"difficult,":[75],"especially":[76],"nonlinear":[78,116,149],"systems.":[79],"These":[80],"tasks":[81],"often":[82],"require":[83],"predefined":[84],"motion":[85],"that":[89,221],"rely":[90],"on":[91,272],"coarse":[92],"approximations":[93],"extensive":[95,270],"manual":[96],"tuning.":[97],"this":[99],"paper,":[100],"we":[101],"propose":[102],"DL-KF,":[103],"robust":[105],"Deep":[106],"Learning":[107],"aided":[108],"Filter":[110],"designed":[111],"perform":[113],"KF":[114],"environments":[118],"where":[119],"process":[121,150],"unavailable":[124],"noise":[128,174],"also":[130],"unknown.":[131],"However,":[132],"linear":[134],"assumed.":[138],"DL-KF":[139,222],"utilizes":[140],"Long":[142],"Short-Term":[143],"Memory":[144],"(LSTM)":[145],"network":[146],"capture":[148],"generate":[153],"priors":[155],"historical":[157],"measurements.":[158],"Additionally,":[159],"Gated":[161],"Recurrent":[162],"Unit":[163],"(GRU)":[164],"independently":[168],"learn":[169],"innovation":[170],"covariance,":[171],"addressing":[172],"uncertainty,":[175],"followed":[176],"by":[177],"computation":[179],"gain":[183],"within":[184],"framework.":[188],"By":[189],"integrating":[190],"an":[191],"SSM":[192],"framework":[193],"with":[194],"deep":[195],"learning":[196],"modules,":[197],"proposed":[199],"method":[200],"strikes":[201],"balance":[203],"between":[204],"flexibility":[206],"data-driven":[208],"approach":[209],"interpretability":[212],"classical":[214],"based":[216],"approaches.":[217],"Empirical":[218],"results":[219],"demonstrate":[220],"significantly":[223],"outperforms":[224],"traditional":[225],"model-based":[226],"filters,":[227],"including":[228],"KF,":[230],"EKF,":[231],"UKF.":[233],"Furthermore,":[234],"it":[235],"surpasses":[236],"state-of-the-art":[237],"hybrid":[238],"methods":[239],"such":[240],"as":[241],"KalmanNet,":[242],"Split-KalmanNet,":[243],"AKNet,":[245],"particularly":[246],"challenging":[248],"scenarios":[249],"involving":[250],"high":[251],"nonlinearity":[252],"(e.g.,":[253],"Lorenz":[255],"attractor)":[256],"maneuver-induced":[258],"mismatches.":[260],"The":[261],"algorithm\u2019s":[262],"efficacy":[263],"robustness":[265],"further":[267],"validated":[268],"through":[269],"experiments":[271],"real-world":[273],"tunnel":[274],"radar":[275],"NCLT":[277],"datasets.":[278]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2026-07-16T13:24:37.021932","created_date":"2026-03-11T00:00:00"}