{"id":"https://openalex.org/W7118369198","doi":"https://doi.org/10.1109/vtc2025-fall65116.2025.11310064","title":"Multimodal Model Based NLOS Identification for Ultra-Wideband Ranging","display_name":"Multimodal Model Based NLOS Identification for Ultra-Wideband Ranging","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W7118369198","doi":"https://doi.org/10.1109/vtc2025-fall65116.2025.11310064"},"language":null,"primary_location":{"id":"doi:10.1109/vtc2025-fall65116.2025.11310064","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2025-fall65116.2025.11310064","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 102nd Vehicular Technology Conference (VTC2025-Fall)","raw_type":"proceedings-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/A5103238963","display_name":"X. Wang","orcid":"https://orcid.org/0009-0004-0506-4940"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingkun Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122212136","display_name":"Shengchu Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengchu Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122020604","display_name":"Yingnan Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingnan Zhou","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122285176","display_name":"Ling Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210131005","display_name":"Chery Automobile (China)","ror":"https://ror.org/02xab7z06","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210131005"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Zhu","raw_affiliation_strings":["Zhejiang ZEEKER Automobile Research & Development Co., Ltd.,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang ZEEKER Automobile Research & Development Co., Ltd.,Beijing,China","institution_ids":["https://openalex.org/I4210131005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122170987","display_name":"LiLi Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210131005","display_name":"Chery Automobile (China)","ror":"https://ror.org/02xab7z06","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210131005"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"LiLi Wang","raw_affiliation_strings":["Zhejiang ZEEKER Automobile Research & Development Co., Ltd.,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang ZEEKER Automobile Research & Development Co., Ltd.,Beijing,China","institution_ids":["https://openalex.org/I4210131005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122025503","display_name":"Yu Jia","orcid":null},"institutions":[{"id":"https://openalex.org/I4210131005","display_name":"Chery Automobile (China)","ror":"https://ror.org/02xab7z06","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210131005"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Jia","raw_affiliation_strings":["Zhejiang ZEEKER Automobile Research & Development Co., Ltd.,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang ZEEKER Automobile Research & Development Co., Ltd.,Beijing,China","institution_ids":["https://openalex.org/I4210131005"]}]}],"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":0.53141857,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.7949000000953674,"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":0.7949000000953674,"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":0.08990000188350677,"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":0.03350000083446503,"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":0.8188999891281128},{"id":"https://openalex.org/keywords/non-line-of-sight-propagation","display_name":"Non-line-of-sight propagation","score":0.8086000084877014},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5878000259399414},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5202999711036682},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4839000105857849},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.44269999861717224},{"id":"https://openalex.org/keywords/transceiver","display_name":"Transceiver","score":0.4187000095844269},{"id":"https://openalex.org/keywords/impulse-response","display_name":"Impulse response","score":0.39800000190734863}],"concepts":[{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.8188999891281128},{"id":"https://openalex.org/C154910267","wikidata":"https://www.wikidata.org/wiki/Q1740982","display_name":"Non-line-of-sight propagation","level":3,"score":0.8086000084877014},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7605000138282776},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5878000259399414},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5583000183105469},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5202999711036682},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4839000105857849},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.44269999861717224},{"id":"https://openalex.org/C7720470","wikidata":"https://www.wikidata.org/wiki/Q954187","display_name":"Transceiver","level":3,"score":0.4187000095844269},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3986000120639801},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.39800000190734863},{"id":"https://openalex.org/C72279823","wikidata":"https://www.wikidata.org/wiki/Q1139726","display_name":"Impulse response","level":2,"score":0.39800000190734863},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.3499999940395355},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.33889999985694885},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.32989999651908875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3208000063896179},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30970001220703125},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2752000093460083},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C70836080","wikidata":"https://www.wikidata.org/wiki/Q837940","display_name":"Impulse (physics)","level":2,"score":0.2538999915122986},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2025-fall65116.2025.11310064","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2025-fall65116.2025.11310064","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 102nd Vehicular Technology Conference (VTC2025-Fall)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.4422447085380554}],"awards":[],"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":13,"referenced_works":["https://openalex.org/W2768409039","https://openalex.org/W2793043725","https://openalex.org/W2970231061","https://openalex.org/W3033032209","https://openalex.org/W3104421441","https://openalex.org/W3129694821","https://openalex.org/W3154148285","https://openalex.org/W4285070404","https://openalex.org/W4312939894","https://openalex.org/W4362590838","https://openalex.org/W4388691938","https://openalex.org/W4396523458","https://openalex.org/W4402778507"],"related_works":[],"abstract_inverted_index":{"In":[0],"complex":[1],"indoor":[2],"environments,":[3],"non-line-of-sight":[4],"(NLOS)":[5],"propagation":[6],"severely":[7],"degrades":[8],"the":[9,39,45,57,74,144,165],"precision":[10],"of":[11],"ultra-wideband":[12],"(UWB)":[13],"ranging.":[14],"Existing":[15],"NLOS":[16,40,127],"identification":[17],"methods":[18,169],"primarily":[19],"rely":[20],"on":[21,73,149],"statistical":[22],"features":[23,63],"or":[24],"waveform":[25],"analysis":[26],"from":[27],"single-modal":[28],"channel":[29],"impulse":[30],"response":[31],"(CIR)":[32],"data,":[33],"but":[34],"could":[35],"be":[36,66],"failed":[37],"when":[38],"CIR":[41,69,99],"is":[42],"similar":[43],"as":[44],"LOS":[46],"one.":[47],"Fortunately,":[48],"image":[49],"vision":[50],"can":[51,65],"provide":[52],"more":[53],"abundant":[54],"information":[55],"about":[56],"ranging":[58],"environment":[59],"and":[60,162],"offer":[61],"spatial":[62],"that":[64,158],"coordinated":[67],"with":[68,109],"temporal":[70],"characteristics.":[71],"Based":[72],"above":[75],"insight,":[76],"this":[77,133],"paper":[78,134],"proposes":[79],"a":[80,121,137,150],"multimodal":[81,145],"collaborative":[82],"perception":[83],"framework":[84],"(MCPF).":[85],"Transceiver":[86],"side":[87],"images":[88],"are":[89],"compressed":[90],"into":[91],"embeddings":[92],"by":[93,174],"convolutional":[94,106],"neural":[95],"networks":[96],"(CNNs),":[97],"while":[98],"acquires":[100],"embedded":[101],"representations":[102],"through":[103],"1":[104],"dimensional":[105],"layers":[107],"combined":[108],"LSTM":[110],"modules.":[111],"Adaptive":[112],"weight":[113],"allocation":[114],"dynamically":[115],"fuses":[116],"these":[117],"cross-modal":[118,131],"features,":[119],"enabling":[120],"lightweight":[122],"fully-connected":[123],"classifier":[124],"to":[125],"distinguish":[126],"conditions.":[128],"To":[129],"optimize":[130],"interactions,":[132],"further":[135],"designs":[136],"tailored":[138],"composite":[139],"loss":[140],"function":[141],"specifically":[142],"for":[143],"architecture.":[146],"Experimental":[147],"validation":[148],"field-collected":[151],"dataset":[152],"spanning":[153],"eight":[154],"LOS/NLOS":[155],"scenarios":[156],"demonstrates":[157],"MCRF":[159],"reaches":[160],"94.31%,":[161],"significantly":[163],"outperforms":[164],"classical":[166],"single":[167],"modal":[168],"(e.g.,":[170],"kurtosis,":[171],"LSTM,":[172],"CNN-LSTM)":[173],"10.59-26.53":[175],"percentages.":[176]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-01-08T00:00:00"}