{"id":"https://openalex.org/W4206378586","doi":"https://doi.org/10.1109/milcom52596.2021.9653015","title":"Deep GEM-Based Network for Weakly Supervised UWB Ranging Error Mitigation","display_name":"Deep GEM-Based Network for Weakly Supervised UWB Ranging Error Mitigation","publication_year":2021,"publication_date":"2021-11-29","ids":{"openalex":"https://openalex.org/W4206378586","doi":"https://doi.org/10.1109/milcom52596.2021.9653015"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/milcom52596.2021.9653015","pdf_url":null,"source":{"id":"https://openalex.org/S4363608114","display_name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://arxiv.org/pdf/2305.13904","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044386909","display_name":"Yuxiao Li","orcid":"https://orcid.org/0000-0002-6496-9991"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxiao Li","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063455689","display_name":"Santiago Mazuelas","orcid":"https://orcid.org/0000-0002-6608-8581"},"institutions":[{"id":"https://openalex.org/I110594554","display_name":"Ikerbasque","ror":"https://ror.org/01cc3fy72","country_code":"ES","type":"other","lineage":["https://openalex.org/I110594554"]},{"id":"https://openalex.org/I2802176441","display_name":"Basque Center for Applied Mathematics","ror":"https://ror.org/03b21sh32","country_code":"ES","type":"education","lineage":["https://openalex.org/I2802176441"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Santiago Mazuelas","raw_affiliation_strings":["BCAM-Basque Center for Applied Mathematics, IKERBASQUE-Basque Foundation for Science, Bilbao, Spain"],"affiliations":[{"raw_affiliation_string":"BCAM-Basque Center for Applied Mathematics, IKERBASQUE-Basque Foundation for Science, Bilbao, Spain","institution_ids":["https://openalex.org/I110594554","https://openalex.org/I2802176441"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052022779","display_name":"Yuan Shen","orcid":"https://orcid.org/0000-0002-9396-1964"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Shen","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institution_assertions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.102,"has_fulltext":true,"fulltext_origin":"pdf","cited_by_count":3,"citation_normalized_percentile":{"value":0.648652,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":72,"max":76},"biblio":{"volume":null,"issue":null,"first_page":"528","last_page":"532"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9998,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9998,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T12024","display_name":"Ultra-Wideband Communications Technology","score":0.9986,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9922,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/ranging","display_name":"Ranging","score":0.9674721},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.49174145},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised Learning","score":0.41569573}],"concepts":[{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.9674721},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.77573276},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5837897},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5385583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.53425115},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.49174145},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47804695},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.41569573},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1286479},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1110436},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07823831},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/milcom52596.2021.9653015","pdf_url":null,"source":{"id":"https://openalex.org/S4363608114","display_name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.13904","pdf_url":"http://arxiv.org/pdf/2305.13904","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://hdl.handle.net/20.500.11824/1444","pdf_url":"https://bird.bcamath.org/bitstream/20.500.11824/1444/3/Deep_GEM_Based_Network_for_Weakly_Supervised_UWB_Range_Error_Mitigation.pdf","source":{"id":"https://openalex.org/S4306401608","display_name":"BIRD (Basque Center for Applied Mathematics)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2802176441","host_organization_name":"Basque Center for Applied Mathematics","host_organization_lineage":["https://openalex.org/I2802176441"],"host_organization_lineage_names":["Basque Center for Applied Mathematics"],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"publishedVersion","is_accepted":true,"is_published":true},{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2305.13904","pdf_url":"https://arxiv.org/pdf/2305.13904","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2305.13904","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.13904","pdf_url":"http://arxiv.org/pdf/2305.13904","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[{"funder":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China","award_id":"2020YFC1511803"}],"datasets":[],"versions":[],"referenced_works_count":16,"referenced_works":["https://openalex.org/W2075744751","https://openalex.org/W2113971713","https://openalex.org/W2121004630","https://openalex.org/W2146027721","https://openalex.org/W2171695182","https://openalex.org/W2746791238","https://openalex.org/W2790826789","https://openalex.org/W2793043725","https://openalex.org/W2883152037","https://openalex.org/W2883475824","https://openalex.org/W2911506192","https://openalex.org/W2954429912","https://openalex.org/W3009118568","https://openalex.org/W3106678532","https://openalex.org/W4206997633","https://openalex.org/W4210560670"],"related_works":["https://openalex.org/W4390813131","https://openalex.org/W4384112194","https://openalex.org/W4328132048","https://openalex.org/W4312958259","https://openalex.org/W4308259661","https://openalex.org/W2783354812","https://openalex.org/W2349383066","https://openalex.org/W2119768059","https://openalex.org/W2103009189","https://openalex.org/W1969901537"],"abstract_inverted_index":{"Ultra-wideband":[0],"(UWB)-based":[1],"techniques,":[2],"while":[3],"becoming":[4],"mainstream":[5],"approaches":[6],"for":[7,24,54,66,85],"high-accurate":[8],"positioning,":[9],"tend":[10],"to":[11,50],"be":[12],"challenged":[13],"by":[14],"ranging":[15,68,88],"bias":[16],"in":[17,114],"harsh":[18],"environments.":[19],"The":[20],"emerging":[21],"learning-based":[22],"methods":[23,42],"error":[25,69,89],"mitigation":[26,90],"have":[27],"shown":[28],"great":[29],"performance":[30],"improvement":[31],"via":[32],"exploiting":[33],"high":[34,52],"semantic":[35],"features":[36],"from":[37],"raw":[38],"data.":[39],"However,":[40],"these":[41],"rely":[43],"heavily":[44],"on":[45,63,79],"fully":[46],"labeled":[47],"data,":[48],"leading":[49],"a":[51,59,74],"cost":[53],"data":[55],"acquisition.":[56],"We":[57],"present":[58],"learning":[60,76,102],"framework":[61],"based":[62,78],"weak":[64,92],"supervision":[65,116],"UWB":[67,87],"mitigation.":[70],"Specifically,":[71],"we":[72],"propose":[73],"deep":[75,101],"method":[77,95],"the":[80,100,119,122],"generalized":[81],"expectation-maximization":[82],"(GEM)":[83],"algorithm":[84],"robust":[86],"under":[91],"supervision.":[93],"Such":[94],"integrate":[96],"probabilistic":[97],"modeling":[98],"into":[99],"scheme,":[103],"and":[104],"adopt":[105],"weakly":[106],"supervised":[107],"labels":[108],"as":[109],"prior":[110],"information.":[111],"Extensive":[112],"experiments":[113],"various":[115],"scenarios":[117],"illustrate":[118],"superiority":[120],"of":[121],"proposed":[123],"method.":[124]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4206378586","counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2024-12-30T20:02:23.356605","created_date":"2022-01-26"}