{"id":"https://openalex.org/W2917795241","doi":"https://doi.org/10.1109/glocom.2018.8647175","title":"The Application of Manhattan Tangent Distance in Outdoor Fingerprint Localization","display_name":"The Application of Manhattan Tangent Distance in Outdoor Fingerprint Localization","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2917795241","doi":"https://doi.org/10.1109/glocom.2018.8647175","mag":"2917795241"},"language":"en","primary_location":{"id":"doi:10.1109/glocom.2018.8647175","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2018.8647175","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Global Communications Conference (GLOBECOM)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5103122673","display_name":"Zhihe Li","orcid":"https://orcid.org/0009-0002-1158-2387"},"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":"Zhihe Li","raw_affiliation_strings":["Dept. of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. 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":["Dept. of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004646219","display_name":"Jie Wei","orcid":"https://orcid.org/0009-0007-2496-4524"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Wei","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, Beijing, CN"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, Beijing, CN","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006300825","display_name":"Shi Han","orcid":"https://orcid.org/0000-0002-0360-6089"},"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":"Han Shi","raw_affiliation_strings":["Tsinghua University, Beijing, Beijing, CN"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, Beijing, CN","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"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":0.9998999834060669,"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":0.9998999834060669,"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/T10860","display_name":"Speech and Audio Processing","score":0.9970999956130981,"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"}},{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9961000084877014,"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/fingerprint","display_name":"Fingerprint (computing)","score":0.727486252784729},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5477551221847534},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5033654570579529},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4894208014011383},{"id":"https://openalex.org/keywords/fingerprint-recognition","display_name":"Fingerprint recognition","score":0.4592866599559784},{"id":"https://openalex.org/keywords/distance-measurement","display_name":"Distance measurement","score":0.44338247179985046},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3568700850009918}],"concepts":[{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.727486252784729},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5477551221847534},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5033654570579529},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4894208014011383},{"id":"https://openalex.org/C168406668","wikidata":"https://www.wikidata.org/wiki/Q178022","display_name":"Fingerprint recognition","level":3,"score":0.4592866599559784},{"id":"https://openalex.org/C2986158284","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Distance measurement","level":2,"score":0.44338247179985046},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3568700850009918}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/glocom.2018.8647175","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2018.8647175","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Global Communications Conference (GLOBECOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8600000143051147}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1142712111","https://openalex.org/W1546169424","https://openalex.org/W1600737329","https://openalex.org/W1777903561","https://openalex.org/W2053186076","https://openalex.org/W2162056146","https://openalex.org/W2170102584","https://openalex.org/W2607595839","https://openalex.org/W2737886025","https://openalex.org/W4234674466"],"related_works":["https://openalex.org/W2110182821","https://openalex.org/W2126162504","https://openalex.org/W2889329118","https://openalex.org/W2031869223","https://openalex.org/W1594390789","https://openalex.org/W2945952022","https://openalex.org/W4293863368","https://openalex.org/W51958864","https://openalex.org/W2355869368","https://openalex.org/W2315459412"],"abstract_inverted_index":{"Received":[0],"signal":[1],"strength":[2],"(RSS)":[3],"is":[4,28,43],"a":[5,92],"typical":[6],"measurement":[7],"used":[8,41],"for":[9,100],"indoor":[10],"and":[11,20,37,59,78,128],"outdoor":[12,98],"localization":[13,27],"because":[14],"of":[15,23,68,130],"its":[16],"low":[17],"power":[18],"consumption":[19],"no":[21],"requirement":[22],"additional":[24],"mode.":[25],"Fingerprint":[26],"known":[29],"to":[30,65,126],"have":[31,51],"higher":[32],"accuracy":[33,67],"than":[34],"traditional":[35],"methods":[36],"the":[38,107,116],"most":[39],"frequently":[40],"algorithm":[42],"K":[44],"Nearest":[45],"Neighbors":[46],"(KNN),":[47],"where":[48,73],"common":[49,133],"metrics":[50],"some":[52],"disadvantages.":[53],"In":[54,86],"this":[55],"paper,":[56],"exponential":[57],"transformation":[58],"tangent":[60,75,81],"distance":[61,76,82],"will":[62],"be":[63],"utilized":[64],"improve":[66],"weighted-KNN":[69],"based":[70],"fingerprint":[71],"localization,":[72],"Manhattan":[74,80],"(MTD)":[77],"approximate":[79],"(AMTD)":[83],"are":[84,89],"proposed.":[85],"addition,":[87],"data":[88],"collected":[90],"by":[91],"field":[93],"tester":[94],"from":[95],"three":[96],"different":[97],"area":[99],"test.":[101],"Experiments":[102],"demonstrate":[103],"that":[104],"MTD":[105,127],"achieves":[106,115],"lowest":[108,118],"root":[109],"mean":[110],"square":[111],"error":[112],"(RMSE),":[113],"AMTD":[114],"second":[117],"RMSE":[119],"with":[120],"much":[121],"lower":[122],"computation":[123],"complexity":[124],"compared":[125],"both":[129],"them":[131],"outperform":[132],"metrics.":[134]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}