{"id":"https://openalex.org/W3039799827","doi":"https://doi.org/10.1109/vtc2020-spring48590.2020.9128821","title":"RSS-based Indoor Passive Localization Using Clustering and Filtering in a LTE Network","display_name":"RSS-based Indoor Passive Localization Using Clustering and Filtering in a LTE Network","publication_year":2020,"publication_date":"2020-05-01","ids":{"openalex":"https://openalex.org/W3039799827","doi":"https://doi.org/10.1109/vtc2020-spring48590.2020.9128821","mag":"3039799827"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2020-spring48590.2020.9128821","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2020-spring48590.2020.9128821","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)","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/A5103164875","display_name":"Huiwen Zheng","orcid":"https://orcid.org/0000-0001-5522-355X"},"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":true,"raw_author_name":"Huiwen Zheng","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/A5100681428","display_name":"Xiaofeng Zhong","orcid":"https://orcid.org/0000-0003-3642-7578"},"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":"Xiaofeng Zhong","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":"last","author":{"id":"https://openalex.org/A5100346767","display_name":"Peng Liu","orcid":"https://orcid.org/0000-0001-8694-7091"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng Liu","raw_affiliation_strings":["Beijing Intersai Technology Co., Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Intersai Technology Co., Ltd., Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103164875"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.411,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.60747995,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"18","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"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":1.0,"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/T11192","display_name":"Underwater Vehicles and Communication Systems","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T10860","display_name":"Speech and Audio Processing","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/fingerprint","display_name":"Fingerprint (computing)","score":0.7986762523651123},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7891805171966553},{"id":"https://openalex.org/keywords/rss","display_name":"RSS","score":0.6980859041213989},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6072167754173279},{"id":"https://openalex.org/keywords/fingerprint-recognition","display_name":"Fingerprint recognition","score":0.5990788340568542},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5622056126594543},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5153000354766846},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48161572217941284},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4766170084476471},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.45046964287757874},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4477333426475525},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4216160178184509},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40213507413864136},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09868261218070984},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07501283288002014}],"concepts":[{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.7986762523651123},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7891805171966553},{"id":"https://openalex.org/C2385561","wikidata":"https://www.wikidata.org/wiki/Q45432","display_name":"RSS","level":2,"score":0.6980859041213989},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6072167754173279},{"id":"https://openalex.org/C168406668","wikidata":"https://www.wikidata.org/wiki/Q178022","display_name":"Fingerprint recognition","level":3,"score":0.5990788340568542},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5622056126594543},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5153000354766846},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48161572217941284},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4766170084476471},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.45046964287757874},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4477333426475525},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4216160178184509},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40213507413864136},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09868261218070984},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07501283288002014},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2020-spring48590.2020.9128821","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2020-spring48590.2020.9128821","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1506974941","https://openalex.org/W1569380410","https://openalex.org/W1891938465","https://openalex.org/W2006036128","https://openalex.org/W2080494495","https://openalex.org/W2108422006","https://openalex.org/W2147628505","https://openalex.org/W2265608995","https://openalex.org/W2278572312","https://openalex.org/W2291859485","https://openalex.org/W2326347197","https://openalex.org/W2550856709","https://openalex.org/W2567370096","https://openalex.org/W2716882564","https://openalex.org/W2763697523","https://openalex.org/W2886808313","https://openalex.org/W2916031314","https://openalex.org/W2964029185","https://openalex.org/W2991392166"],"related_works":["https://openalex.org/W3014822659","https://openalex.org/W4362496757","https://openalex.org/W4389371618","https://openalex.org/W2117826006","https://openalex.org/W2566091814","https://openalex.org/W2114937328","https://openalex.org/W2148654711","https://openalex.org/W2608025327","https://openalex.org/W1621827506","https://openalex.org/W2350223345"],"abstract_inverted_index":{"Nowadays,":[0],"fingerprint":[1,18,72],"positioning":[2,9,125],"is":[3,13,99],"the":[4,21,35,78,84,91,104,116,124,132],"mainstream":[5],"method":[6],"in":[7,17,48,94,107,115],"indoor":[8,108],"and":[10,34,51,71,76,130],"Weighted-K-Nearest-Neighbor":[11],"(WKNN)":[12],"most":[14],"widely":[15],"used":[16,81],"matching.":[19],"However,":[20],"fingerprints":[22,37,43],"which":[23,46],"are":[24,80],"far":[25],"away":[26],"from":[27],"each":[28],"other":[29],"might":[30],"also":[31],"be":[32,103],"similar":[33],"target":[36],"have":[38],"to":[39,62,82,102],"match":[40],"with":[41,65,136],"all":[42],"every":[44],"time,":[45],"results":[47],"unsatisfactory":[49],"accuracy":[50],"efficiency":[52],"of":[53,68],"WKNN.":[54,137],"In":[55,86],"this":[56],"paper,":[57],"we":[58,88],"propose":[59,89],"an":[60],"algorithm":[61,121],"classify":[63],"regions":[64],"comprehensive":[66],"consideration":[67],"geographic":[69],"location":[70],"similarity,":[73],"meanwhile":[74],"preprocessing":[75],"filtering":[77],"data":[79],"improve":[83],"accuracy.":[85],"addition,":[87],"that":[90,114],"cellular":[92],"signal":[93,105],"Long":[95],"Term":[96],"Evolution":[97],"(LTE)":[98],"more":[100],"suitable":[101],"source":[106],"passive":[109],"localization":[110],"scenario.":[111],"Results":[112],"show":[113],"LTE":[117],"environment,":[118],"our":[119],"proposed":[120],"effectively":[122],"reduces":[123],"error":[126],"by":[127],"about":[128],"24%":[129],"improves":[131],"convergence":[133],"speed":[134],"compared":[135]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
