{"id":"https://openalex.org/W4402892225","doi":"https://doi.org/10.1109/tmc.2024.3469252","title":"Multi-Modal Image and Radio Frequency Fusion for Optimizing Vehicle Positioning","display_name":"Multi-Modal Image and Radio Frequency Fusion for Optimizing Vehicle Positioning","publication_year":2024,"publication_date":"2024-09-26","ids":{"openalex":"https://openalex.org/W4402892225","doi":"https://doi.org/10.1109/tmc.2024.3469252"},"language":"en","primary_location":{"id":"doi:10.1109/tmc.2024.3469252","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmc.2024.3469252","pdf_url":null,"source":{"id":"https://openalex.org/S69141925","display_name":"IEEE Transactions on Mobile Computing","issn_l":"1536-1233","issn":["1536-1233","1558-0660","2161-9875"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Transactions on Mobile Computing","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/A5106953746","display_name":"Ouwen Huan","orcid":"https://orcid.org/0009-0003-4693-225X"},"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":"Ouwen Huan","raw_affiliation_strings":["Beijing Laboratory of Advanced Information Network, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0003-4693-225X","affiliations":[{"raw_affiliation_string":"Beijing Laboratory of Advanced Information Network, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002713029","display_name":"Tao Luo","orcid":"https://orcid.org/0000-0003-4870-5942"},"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":"Tao Luo","raw_affiliation_strings":["Beijing Laboratory of Advanced Information Network, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4870-5942","affiliations":[{"raw_affiliation_string":"Beijing Laboratory of Advanced Information Network, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072241033","display_name":"Mingzhe Chen","orcid":"https://orcid.org/0000-0003-2570-703X"},"institutions":[{"id":"https://openalex.org/I145608581","display_name":"University of Miami","ror":"https://ror.org/02dgjyy92","country_code":"US","type":"education","lineage":["https://openalex.org/I145608581"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingzhe Chen","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Institute for Data Science and Computing, University of Miami, Coral Gables, FL, USA","Department of Electrical and Computer Engineering and Institute for Data Science and Computing, University of Miami, Coral Gables, FL, USA"],"raw_orcid":"https://orcid.org/0000-0003-2570-703X","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Institute for Data Science and Computing, University of Miami, Coral Gables, FL, USA","institution_ids":["https://openalex.org/I145608581"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering and Institute for Data Science and Computing, University of Miami, Coral Gables, FL, USA","institution_ids":["https://openalex.org/I145608581"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8609,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.77630462,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"24","issue":"2","first_page":"696","last_page":"708"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9588000178337097,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9348000288009644,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/computer-science","display_name":"Computer science","score":0.7373465895652771},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4720122516155243},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.4701329171657562},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.43947741389274597},{"id":"https://openalex.org/keywords/radio-frequency","display_name":"Radio frequency","score":0.42875608801841736},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40860557556152344},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.3431168794631958},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.32550549507141113}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7373465895652771},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4720122516155243},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.4701329171657562},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.43947741389274597},{"id":"https://openalex.org/C74064498","wikidata":"https://www.wikidata.org/wiki/Q3396184","display_name":"Radio frequency","level":2,"score":0.42875608801841736},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40860557556152344},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.3431168794631958},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32550549507141113},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmc.2024.3469252","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmc.2024.3469252","pdf_url":null,"source":{"id":"https://openalex.org/S69141925","display_name":"IEEE Transactions on Mobile Computing","issn_l":"1536-1233","issn":["1536-1233","1558-0660","2161-9875"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Transactions on Mobile Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2015320000","display_name":null,"funder_award_id":"62171047","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":43,"referenced_works":["https://openalex.org/W1932847118","https://openalex.org/W2140502378","https://openalex.org/W2194775991","https://openalex.org/W2205690070","https://openalex.org/W2309512289","https://openalex.org/W2809356669","https://openalex.org/W2865103122","https://openalex.org/W2891244534","https://openalex.org/W2963037989","https://openalex.org/W2971911836","https://openalex.org/W3022866026","https://openalex.org/W3039059058","https://openalex.org/W3090615085","https://openalex.org/W3109847748","https://openalex.org/W3129252918","https://openalex.org/W3156724822","https://openalex.org/W3158325475","https://openalex.org/W3160247247","https://openalex.org/W3160417706","https://openalex.org/W3169995643","https://openalex.org/W3175919531","https://openalex.org/W3194668692","https://openalex.org/W3199469864","https://openalex.org/W3203789000","https://openalex.org/W3209374810","https://openalex.org/W3210861968","https://openalex.org/W4214586471","https://openalex.org/W4285264320","https://openalex.org/W4293150274","https://openalex.org/W4293197643","https://openalex.org/W4315777499","https://openalex.org/W4315783169","https://openalex.org/W4317038447","https://openalex.org/W4319303001","https://openalex.org/W4323793963","https://openalex.org/W4362583003","https://openalex.org/W4376466684","https://openalex.org/W4376480850","https://openalex.org/W4385324865","https://openalex.org/W4387448603","https://openalex.org/W4392979759","https://openalex.org/W6750523955","https://openalex.org/W6773228540"],"related_works":["https://openalex.org/W2379392295","https://openalex.org/W3160965418","https://openalex.org/W613940353","https://openalex.org/W2320915480","https://openalex.org/W2362990116","https://openalex.org/W2373946551","https://openalex.org/W2032636564","https://openalex.org/W2350275110","https://openalex.org/W2258043314","https://openalex.org/W2381578981"],"abstract_inverted_index":{"In":[0,21],"this":[1],"paper,":[2],"a":[3,55,64,70,131,161,174,224],"multi-modal":[4],"vehicle":[5,30,118,150,237],"positioning":[6,216],"framework":[7],"that":[8,60,209,226],"jointly":[9],"localizes":[10],"vehicles":[11],"with":[12,33,54],"channel":[13],"state":[14],"information":[15],"(CSI)":[16],"and":[17,37,76,88,115,149,155,169,198,231],"images":[18,78,154,230],"is":[19,52],"designed.":[20],"particular,":[22],"we":[23,94,105,122,159],"consider":[24],"an":[25,96],"outdoor":[26],"scenario":[27],"where":[28],"each":[29],"can":[31,40,62,213],"communicate":[32],"only":[34,46,233],"one":[35],"BS,":[36],"hence,":[38],"it":[39,61],"upload":[41],"its":[42,47],"estimated":[43],"CSI":[44,86,114,148,196,234],"to":[45,189,220,223],"associated":[48],"BS.":[49],"Each":[50],"BS":[51],"equipped":[53],"set":[56],"of":[57,67,73,126,139,173,194],"cameras,":[58],"such":[59],"collect":[63],"small":[65],"number":[66,72],"labeled":[68],"CSI,":[69,75],"large":[71],"unlabeled":[74,85,113,147,167,195],"the":[77,84,109,116,124,127,137,166,171,179,183,190,210,215],"taken":[79],"by":[80,143,218],"cameras.":[81],"To":[82,135],"exploit":[83],"data":[87],"position":[89,201],"labels":[90,202],"obtained":[91,152,203],"from":[92,153,204],"images,":[93,121],"design":[95],"meta-learning":[97,175],"based":[98],"hard":[99],"expectation-maximization":[100],"(EM)":[101],"algorithm.":[102],"Specifically,":[103],"since":[104],"do":[106],"not":[107,228],"know":[108],"corresponding":[110],"relationship":[111],"between":[112,146],"multiple":[117],"locations":[119,151],"in":[120],"formulate":[123],"calculation":[125],"training":[128],"objective":[129],"as":[130],"minimum":[132],"matching":[133,145],"problem.":[134],"reduce":[136,214],"impact":[138],"label":[140],"noises":[141],"caused":[142],"incorrect":[144],"achieve":[156],"better":[157],"convergence,":[158],"introduce":[160],"weighted":[162,180,191],"loss":[163,192],"function":[164,193],"on":[165],"datasets,":[168],"study":[170],"use":[172,229],"algorithm":[176],"for":[177,236],"computing":[178],"loss.":[181],"Subsequently,":[182],"model":[184],"parameters":[185],"are":[186],"updated":[187],"according":[188],"samples":[197],"their":[199],"matched":[200],"images.":[205],"Simulation":[206],"results":[207],"show":[208],"proposed":[211],"method":[212],"error":[217],"up":[219],"61%":[221],"compared":[222],"baseline":[225],"does":[227],"uses":[232],"fingerprint":[235],"positioning.":[238]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
