{"id":"https://openalex.org/W4417438449","doi":"https://doi.org/10.1109/lsp.2025.3645187","title":"Finetuning the Sample Points in Gaussian Filters via Neural Networks","display_name":"Finetuning the Sample Points in Gaussian Filters via Neural Networks","publication_year":2025,"publication_date":"2025-12-17","ids":{"openalex":"https://openalex.org/W4417438449","doi":"https://doi.org/10.1109/lsp.2025.3645187"},"language":null,"primary_location":{"id":"doi:10.1109/lsp.2025.3645187","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3645187","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/A5100608407","display_name":"Hanyu Liu","orcid":"https://orcid.org/0000-0002-8898-3683"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanyu Liu","raw_affiliation_strings":["School of Astronautics, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8898-3683","affiliations":[{"raw_affiliation_string":"School of Astronautics, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yuran Chen","orcid":"https://orcid.org/0009-0007-9224-9768"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuran Chen","raw_affiliation_strings":["School of Future Aerospace Technology, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-9224-9768","affiliations":[{"raw_affiliation_string":"School of Future Aerospace Technology, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038896867","display_name":"Xiucong Sun","orcid":"https://orcid.org/0000-0001-8033-5799"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiucong Sun","raw_affiliation_strings":["School of Astronautics, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8033-5799","affiliations":[{"raw_affiliation_string":"School of Astronautics, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114950232","display_name":"Yukai Zhu","orcid":"https://orcid.org/0009-0007-9374-3143"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yukai Zhu","raw_affiliation_strings":["School of Astronautics, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-9374-3143","affiliations":[{"raw_affiliation_string":"School of Astronautics, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xinlong Wang","orcid":"https://orcid.org/0000-0002-2122-8763"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinlong Wang","raw_affiliation_strings":["School of Astronautics, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2122-8763","affiliations":[{"raw_affiliation_string":"School of Astronautics, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069886390","display_name":"Haichao Gui","orcid":"https://orcid.org/0000-0002-0175-6104"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haichao Gui","raw_affiliation_strings":["School of Astronautics, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0175-6104","affiliations":[{"raw_affiliation_string":"School of Astronautics, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19290201,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":null,"first_page":"371","last_page":"375"},"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":0.9790999889373779,"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":0.9790999889373779,"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/T11325","display_name":"Inertial Sensor and Navigation","score":0.003599999938160181,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.002400000113993883,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/gaussian","display_name":"Gaussian","score":0.6538000106811523},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.5587000250816345},{"id":"https://openalex.org/keywords/gaussian-filter","display_name":"Gaussian filter","score":0.5533000230789185},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5192999839782715},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.48330000042915344},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.43619999289512634},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.4350999891757965},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.43320000171661377}],"concepts":[{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6538000106811523},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.5587000250816345},{"id":"https://openalex.org/C65892221","wikidata":"https://www.wikidata.org/wiki/Q1113935","display_name":"Gaussian filter","level":3,"score":0.5533000230789185},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5368000268936157},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5252000093460083},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5192999839782715},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.48330000042915344},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.43619999289512634},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4350999891757965},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.43320000171661377},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41769999265670776},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4165000021457672},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4050999879837036},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3882000148296356},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.38420000672340393},{"id":"https://openalex.org/C79334102","wikidata":"https://www.wikidata.org/wiki/Q3072268","display_name":"Ensemble Kalman filter","level":4,"score":0.3815999925136566},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37139999866485596},{"id":"https://openalex.org/C50151734","wikidata":"https://www.wikidata.org/wiki/Q1759577","display_name":"Matched filter","level":3,"score":0.36739999055862427},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.28040000796318054},{"id":"https://openalex.org/C8639503","wikidata":"https://www.wikidata.org/wiki/Q6059511","display_name":"Invariant extended Kalman filter","level":4,"score":0.2687000036239624},{"id":"https://openalex.org/C139399703","wikidata":"https://www.wikidata.org/wiki/Q7897426","display_name":"Unscented transform","level":5,"score":0.2574999928474426},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C137685913","wikidata":"https://www.wikidata.org/wiki/Q4316763","display_name":"Nonlinear filter","level":4,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2025.3645187","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3645187","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/G3502894612","display_name":null,"funder_award_id":"GW2025-61","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"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":25,"referenced_works":["https://openalex.org/W1966307629","https://openalex.org/W1982322007","https://openalex.org/W1983180205","https://openalex.org/W2043357888","https://openalex.org/W2054091988","https://openalex.org/W2063052942","https://openalex.org/W2099371695","https://openalex.org/W2099544223","https://openalex.org/W2123487311","https://openalex.org/W2144144709","https://openalex.org/W2146332636","https://openalex.org/W2150355110","https://openalex.org/W2603020053","https://openalex.org/W2753916640","https://openalex.org/W2965291815","https://openalex.org/W3213048151","https://openalex.org/W4236076921","https://openalex.org/W4383097042","https://openalex.org/W4391092555","https://openalex.org/W4392593943","https://openalex.org/W4399849857","https://openalex.org/W4403331936","https://openalex.org/W4404442421","https://openalex.org/W4404469541","https://openalex.org/W4407448548"],"related_works":[],"abstract_inverted_index":{"Gaussian":[0,69,95,126,139,160],"filters":[1,33,70,96,161],"with":[2,71,97],"deterministic":[3,98],"sample":[4,39,76,91,99,121,133,168],"points,":[5],"such":[6],"as":[7,166],"the":[8,43,55,59,111,114,120,124,132,138,147,151,159,163,167],"Unscented":[9],"Kalman":[10,14],"Filter":[11,15,20],"(UKF),":[12],"Cubature":[13],"(CKF),":[16],"Gauss\u2013":[17],"Hermite":[18],"Quadrature":[19],"(GHQF),":[21],"etc.,":[22],"have":[23,49],"been":[24],"widely":[25],"employed":[26],"for":[27,89],"nonlinear":[28],"state":[29],"estimation.":[30],"However,":[31],"these":[32,61],"utilize":[34],"a":[35,72,83,105],"fixed":[36],"set":[37],"of":[38,42,58,75,123,150],"points":[40,92,122,134],"irrespective":[41],"system's":[44],"nonlinearity.":[45],"While":[46],"various":[47],"studies":[48],"explored":[50],"data-driven":[51],"approaches":[52],"to":[53,67,109,119,177],"optimize":[54],"three":[56],"parameters":[57],"UKF,":[60],"methods":[62],"do":[63],"not":[64],"generalize":[65],"well":[66],"other":[68],"greater":[73],"number":[74],"points.":[77,100],"In":[78],"this":[79],"letter,":[80],"we":[81,102],"propose":[82],"novel":[84],"neural":[85],"network-based":[86],"unified":[87],"framework":[88],"finetuning":[90],"across":[93],"all":[94],"Specifically,":[101],"first":[103],"pretrain":[104],"Multi-Layer":[106],"Perceptron":[107],"(MLP)":[108],"approximate":[110],"mapping":[112],"from":[113],"state's":[115],"mean":[116],"and":[117,141],"covariance":[118],"original":[125,179],"filter.":[127],"The":[128],"MLP":[129,165],"then":[130],"replaces":[131],"generation":[135,170],"strategy":[136,171],"in":[137],"filter":[140],"is":[142],"further":[143],"refined":[144],"by":[145],"maximizing":[146],"marginal":[148],"likelihood":[149],"observed":[152],"measurement":[153],"data.":[154],"Simulation":[155],"results":[156],"demonstrate":[157],"that":[158],"leveraging":[162],"well-trained":[164],"point":[169],"achieve":[172],"higher":[173],"filtering":[174],"accuracy":[175],"compared":[176],"their":[178],"counterparts.":[180]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-12-17T00:00:00"}
