{"id":"https://openalex.org/W7117747401","doi":"https://doi.org/10.1109/lsp.2025.3650087","title":"Outlier-Robust KalmanNet: Neural Network Aided Kalman Filtering Based on Huber Loss","display_name":"Outlier-Robust KalmanNet: Neural Network Aided Kalman Filtering Based on Huber Loss","publication_year":2025,"publication_date":"2025-12-31","ids":{"openalex":"https://openalex.org/W7117747401","doi":"https://doi.org/10.1109/lsp.2025.3650087"},"language":null,"primary_location":{"id":"doi:10.1109/lsp.2025.3650087","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3650087","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/A5111995398","display_name":"Shaodian Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaodian Liu","raw_affiliation_strings":["School of Internet of Things Engineering, Jiangnan University, Wuxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Internet of Things Engineering, Jiangnan University, Wuxi, China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121647049","display_name":"Xiang Shen","orcid":null},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Shen","raw_affiliation_strings":["School of Internet of Things Engineering, Jiangnan University, Wuxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Internet of Things Engineering, Jiangnan University, Wuxi, China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121687382","display_name":"Dongcheng Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongcheng Zhou","raw_affiliation_strings":["School of Internet of Things Engineering, Jiangnan University, Wuxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Internet of Things Engineering, Jiangnan University, Wuxi, China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061470526","display_name":"Wenxu Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxu Yan","raw_affiliation_strings":["School of Internet of Things Engineering, Jiangnan University, Wuxi, China"],"raw_orcid":"https://orcid.org/0000-0001-6985-9196","affiliations":[{"raw_affiliation_string":"School of Internet of Things Engineering, Jiangnan University, Wuxi, China","institution_ids":["https://openalex.org/I111599522"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5121631302","display_name":"Wenyuan Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I111599522","display_name":"Jiangnan University","ror":"https://ror.org/04mkzax54","country_code":"CN","type":"education","lineage":["https://openalex.org/I111599522"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenyuan Wang","raw_affiliation_strings":["School of Internet of Things Engineering, Jiangnan University, Wuxi, China"],"raw_orcid":"https://orcid.org/0000-0001-8590-7303","affiliations":[{"raw_affiliation_string":"School of Internet of Things Engineering, Jiangnan University, Wuxi, China","institution_ids":["https://openalex.org/I111599522"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I111599522"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.7497636,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":null,"first_page":"476","last_page":"480"},"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":0.5781999826431274,"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":0.5781999826431274,"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/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.1006999984383583,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.07020000368356705,"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/kalman-filter","display_name":"Kalman filter","score":0.8054999709129333},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6115000247955322},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5612999796867371},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5414000153541565},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5328999757766724},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.5267000198364258},{"id":"https://openalex.org/keywords/control-theory","display_name":"Control theory (sociology)","score":0.4936999976634979},{"id":"https://openalex.org/keywords/fast-kalman-filter","display_name":"Fast Kalman filter","score":0.48179998993873596}],"concepts":[{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.8054999709129333},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7038000226020813},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6115000247955322},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5612999796867371},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5414000153541565},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5328999757766724},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5267000198364258},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.4936999976634979},{"id":"https://openalex.org/C150679823","wikidata":"https://www.wikidata.org/wiki/Q5436946","display_name":"Fast Kalman filter","level":4,"score":0.48179998993873596},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.47530001401901245},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.42559999227523804},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.40700000524520874},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3709999918937683},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.35190001130104065},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.34459999203681946},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3163999915122986},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2939000129699707},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.26840001344680786},{"id":"https://openalex.org/C107645828","wikidata":"https://www.wikidata.org/wiki/Q12070446","display_name":"System model","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C8639503","wikidata":"https://www.wikidata.org/wiki/Q6059511","display_name":"Invariant extended Kalman filter","level":4,"score":0.25859999656677246}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2025.3650087","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3650087","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":[{"id":"https://openalex.org/G5597025958","display_name":null,"funder_award_id":"62101215","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2012526720","https://openalex.org/W2016456873","https://openalex.org/W2020934227","https://openalex.org/W2105934661","https://openalex.org/W2142474014","https://openalex.org/W2166787100","https://openalex.org/W2294322297","https://openalex.org/W2553413742","https://openalex.org/W2909911838","https://openalex.org/W2945434604","https://openalex.org/W2997297215","https://openalex.org/W3028365456","https://openalex.org/W3138327325","https://openalex.org/W3163591098","https://openalex.org/W3183282730","https://openalex.org/W4249736682","https://openalex.org/W4322706860","https://openalex.org/W4323065916","https://openalex.org/W4380450972","https://openalex.org/W4403510710","https://openalex.org/W4410808522"],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,20,90,94],"novel":[6],"Kalman":[7,43,55,119],"filtering":[8,114],"framework\u2014Outlier-Robust":[9],"KalmanNet\u2014based":[10],"on":[11,59],"an":[12],"outlier-robust":[13],"recurrent":[14,31],"neural":[15,32],"network":[16,33],"(RNN).":[17],"By":[18],"incorporating":[19],"robust":[21],"training":[22],"strategy":[23],"utilizing":[24],"the":[25,29,42,51,54,63,84,87,117,121],"Huber":[26],"loss":[27],"function,":[28],"proposed":[30,88,122],"effectively":[34,72],"suppresses":[35],"interference":[36],"from":[37],"outliers":[38],"while":[39],"dynamically":[40],"generating":[41],"gain":[44,56],"(KG)":[45],"matrix.":[46],"This":[47],"data-driven":[48],"approach":[49],"enables":[50],"estimation":[52],"of":[53,62,86],"without":[57],"relying":[58],"prior":[60],"knowledge":[61],"process":[64],"and":[65,70,79,116],"observation":[66],"noise":[67,100],"covariance":[68],"matrices,":[69],"it":[71],"addresses":[73],"issues":[74],"related":[75],"to":[76],"system":[77,96],"nonlinearity":[78],"model":[80],"mismatch.":[81],"To":[82],"validate":[83],"effectiveness":[85],"algorithm,":[89],"simulation":[91],"environment":[92],"for":[93,103],"nonlinear":[95],"contaminated":[97],"by":[98],"impulsive":[99],"was":[101],"established":[102],"comparative":[104],"experimental":[105],"analysis.":[106],"Simulation":[107],"results":[108],"demonstrate":[109],"that,":[110],"compared":[111],"with":[112],"classical":[113],"methods":[115],"conventional":[118],"network,":[120],"method":[123],"exhibits":[124],"superior":[125],"robustness.":[126]},"counts_by_year":[],"updated_date":"2026-01-14T23:40:02.550235","created_date":"2025-12-31T00:00:00"}
