{"id":"https://openalex.org/W4413277085","doi":"https://doi.org/10.1109/lsp.2025.3599708","title":"CKFNet: Neural Network Aided Cubature Kalman Filtering","display_name":"CKFNet: Neural Network Aided Cubature Kalman Filtering","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4413277085","doi":"https://doi.org/10.1109/lsp.2025.3599708"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2025.3599708","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3599708","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/A5021407422","display_name":"Jinhui Hu","orcid":"https://orcid.org/0009-0006-9241-7402"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinhui Hu","raw_affiliation_strings":["Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":"https://orcid.org/0009-0006-9241-7402","affiliations":[{"raw_affiliation_string":"Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haiquan Zhao","orcid":"https://orcid.org/0000-0003-0198-1384"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haiquan Zhao","raw_affiliation_strings":["Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0003-0198-1384","affiliations":[{"raw_affiliation_string":"Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101738718","display_name":"Yi Peng","orcid":"https://orcid.org/0009-0001-5001-4035"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Peng","raw_affiliation_strings":["Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":"https://orcid.org/0009-0001-5001-4035","affiliations":[{"raw_affiliation_string":"Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4800084"],"apc_list":null,"apc_paid":null,"fwci":2.792,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.91420199,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"32","issue":null,"first_page":"3455","last_page":"3459"},"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":0.6010000109672546,"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":0.6010000109672546,"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":0.745955228805542},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6846340894699097},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5892261862754822},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4646107256412506},{"id":"https://openalex.org/keywords/fast-kalman-filter","display_name":"Fast Kalman filter","score":0.42986243963241577},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42024755477905273},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.30356907844543457}],"concepts":[{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.745955228805542},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6846340894699097},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5892261862754822},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4646107256412506},{"id":"https://openalex.org/C150679823","wikidata":"https://www.wikidata.org/wiki/Q5436946","display_name":"Fast Kalman filter","level":4,"score":0.42986243963241577},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42024755477905273},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.30356907844543457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2025.3599708","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3599708","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/G3184487484","display_name":null,"funder_award_id":"62171388","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4514581901","display_name":"\u9ad8\u6548\u7a33\u5065\u7684\u5206\u5e03\u5f0f\u7ea6\u675f\u81ea\u9002\u5e94\u6ee4\u6ce2\u65b0\u65b9\u6cd5\u4e0e\u5e94\u7528\u7814\u7a76","funder_award_id":"61871461","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6148365373","display_name":"\u591a\u6838\u81ea\u9002\u5e94\u6ee4\u6ce2\u65b0\u65b9\u6cd5\u53ca\u5e94\u7528\u7814\u7a76","funder_award_id":"61571374","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":23,"referenced_works":["https://openalex.org/W1940221538","https://openalex.org/W1981955701","https://openalex.org/W2054091988","https://openalex.org/W2064675550","https://openalex.org/W2157331557","https://openalex.org/W2411895082","https://openalex.org/W2737188128","https://openalex.org/W2773850766","https://openalex.org/W2963134661","https://openalex.org/W2997297215","https://openalex.org/W3096398821","https://openalex.org/W3112170415","https://openalex.org/W3160206926","https://openalex.org/W3183282730","https://openalex.org/W3209193236","https://openalex.org/W3213627808","https://openalex.org/W4206784501","https://openalex.org/W4281567319","https://openalex.org/W4380450972","https://openalex.org/W4385991848","https://openalex.org/W4392903281","https://openalex.org/W4399073692","https://openalex.org/W4401443851"],"related_works":["https://openalex.org/W2182059946","https://openalex.org/W1580685205","https://openalex.org/W4411451922","https://openalex.org/W2320496747","https://openalex.org/W1488420340","https://openalex.org/W2164174227","https://openalex.org/W4206770590","https://openalex.org/W2375426323","https://openalex.org/W2363340733","https://openalex.org/W2103062922"],"abstract_inverted_index":{"The":[0],"cubature":[1,44,85],"Kalman":[2],"filter":[3],"(CKF),":[4],"while":[5,41],"theoretically":[6],"rigorous":[7],"for":[8],"nonlinear":[9],"estimation,":[10],"often":[11],"suffers":[12],"performance":[13],"degradation":[14],"due":[15],"to":[16,58,61,103],"model-environment":[17],"mismatches":[18],"in":[19,54],"practice.":[20],"To":[21],"address":[22],"this":[23],"limitation,":[24],"we":[25],"propose":[26],"CKFNet-a":[27],"hybrid":[28],"architecture":[29,76],"that":[30,93],"synergistically":[31],"integrates":[32],"recurrent":[33],"neural":[34],"networks":[35],"(RNN)":[36],"with":[37],"the":[38,55,75],"CKF":[39],"framework":[40],"preserving":[42],"its":[43],"principles.":[45],"Unlike":[46],"conventional":[47,104],"modeldriven":[48],"approaches,":[49],"CKFNet":[50,96],"embeds":[51],"RNN":[52],"modules":[53],"prediction":[56],"phase":[57],"dynamically":[59],"adapt":[60],"unmodeled":[62],"uncertainties,":[63],"effectively":[64],"reducing":[65],"cumulative":[66],"error":[67],"propagation":[68],"through":[69],"temporal":[70],"noise":[71],"correlation":[72],"learning.":[73],"Crucially,":[74],"maintains":[77],"CKF's":[78],"analytical":[79],"interpretability":[80],"via":[81],"constrained":[82],"optimization":[83],"of":[84],"point":[86],"distributions.":[87],"Numerical":[88],"simulation":[89],"experiments":[90],"have":[91],"confirmed":[92],"our":[94],"proposed":[95],"exhibits":[97],"superior":[98],"accuracy":[99],"and":[100,107],"robustness":[101],"compared":[102],"model-based":[105],"methods":[106],"existing":[108],"KalmanNet":[109],"algorithms.":[110]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
