{"id":"https://openalex.org/W4200074578","doi":"https://doi.org/10.1109/iccais52680.2021.9624548","title":"An Effective 3D Indoor Localization Approach Based on Fingerprint Fusion Positioning","display_name":"An Effective 3D Indoor Localization Approach Based on Fingerprint Fusion Positioning","publication_year":2021,"publication_date":"2021-10-14","ids":{"openalex":"https://openalex.org/W4200074578","doi":"https://doi.org/10.1109/iccais52680.2021.9624548"},"language":"en","primary_location":{"id":"doi:10.1109/iccais52680.2021.9624548","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccais52680.2021.9624548","pdf_url":null,"source":{"id":"https://openalex.org/S4363608071","display_name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","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/A5100642435","display_name":"Haoyu Yang","orcid":"https://orcid.org/0000-0002-4709-0061"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haoyu Yang","raw_affiliation_strings":["Key Lab. Inform. Fusion Tech., Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Key Lab. Inform. Fusion Tech., Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100430498","display_name":"Zheng Hu","orcid":"https://orcid.org/0000-0001-5008-6015"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Hu","raw_affiliation_strings":["Key Lab. Inform. Fusion Tech., Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Key Lab. Inform. Fusion Tech., Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056814645","display_name":"DongChen Li","orcid":null},"institutions":[{"id":"https://openalex.org/I147230869","display_name":"China Shipbuilding Industry Corporation (China)","ror":"https://ror.org/0410k9915","country_code":"CN","type":"company","lineage":["https://openalex.org/I147230869"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"DongChen Li","raw_affiliation_strings":["China Shipbuilding Industry, Systems Engineering Research Institute, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China Shipbuilding Industry, Systems Engineering Research Institute, Beijing, China","institution_ids":["https://openalex.org/I147230869"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100728697","display_name":"Tiancheng Li","orcid":"https://orcid.org/0000-0002-0499-5135"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tiancheng Li","raw_affiliation_strings":["Key Lab. Inform. Fusion Tech., Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Key Lab. Inform. Fusion Tech., Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100642435"],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":0.4327,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.45615207,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"892","last_page":"897"},"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9926999807357788,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/multilateration","display_name":"Multilateration","score":0.8246550559997559},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7302306890487671},{"id":"https://openalex.org/keywords/fingerprint","display_name":"Fingerprint (computing)","score":0.6077814102172852},{"id":"https://openalex.org/keywords/hypermarket","display_name":"Hypermarket","score":0.5688745379447937},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4845982789993286},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.4799483120441437},{"id":"https://openalex.org/keywords/fingerprint-recognition","display_name":"Fingerprint recognition","score":0.4447319805622101},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.42994844913482666},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4125574231147766},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.41158726811408997},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.392511248588562},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38925811648368835}],"concepts":[{"id":"https://openalex.org/C104037064","wikidata":"https://www.wikidata.org/wiki/Q1640884","display_name":"Multilateration","level":3,"score":0.8246550559997559},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7302306890487671},{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.6077814102172852},{"id":"https://openalex.org/C56084127","wikidata":"https://www.wikidata.org/wiki/Q750656","display_name":"Hypermarket","level":2,"score":0.5688745379447937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4845982789993286},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4799483120441437},{"id":"https://openalex.org/C168406668","wikidata":"https://www.wikidata.org/wiki/Q178022","display_name":"Fingerprint recognition","level":3,"score":0.4447319805622101},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.42994844913482666},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4125574231147766},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.41158726811408997},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.392511248588562},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38925811648368835},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccais52680.2021.9624548","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccais52680.2021.9624548","pdf_url":null,"source":{"id":"https://openalex.org/S4363608071","display_name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Control, Automation and Information Sciences (ICCAIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G3246475613","display_name":null,"funder_award_id":"62071389","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":29,"referenced_works":["https://openalex.org/W1542766183","https://openalex.org/W1642358959","https://openalex.org/W1971139493","https://openalex.org/W1985141254","https://openalex.org/W2006276128","https://openalex.org/W2009714809","https://openalex.org/W2051376734","https://openalex.org/W2072476227","https://openalex.org/W2119362555","https://openalex.org/W2119539043","https://openalex.org/W2120350100","https://openalex.org/W2154347068","https://openalex.org/W2163388754","https://openalex.org/W2166114007","https://openalex.org/W2278572312","https://openalex.org/W2280812320","https://openalex.org/W2309512289","https://openalex.org/W2491151330","https://openalex.org/W2562522049","https://openalex.org/W2562764869","https://openalex.org/W2783754079","https://openalex.org/W2787139105","https://openalex.org/W2800970815","https://openalex.org/W2895240804","https://openalex.org/W2901002920","https://openalex.org/W2908979191","https://openalex.org/W2963322744","https://openalex.org/W2998294810","https://openalex.org/W3117556779"],"related_works":["https://openalex.org/W3014822659","https://openalex.org/W4362496757","https://openalex.org/W2566091814","https://openalex.org/W4389371618","https://openalex.org/W2051501574","https://openalex.org/W2117826006","https://openalex.org/W2114937328","https://openalex.org/W2148654711","https://openalex.org/W2608025327","https://openalex.org/W1621827506"],"abstract_inverted_index":{"We":[0,48,92],"propose":[1],"an":[2],"effective":[3],"target":[4,45,57,76],"locating":[5],"approach":[6,118],"based":[7],"on":[8,70,89],"the":[9,17,23,28,43,56,61,90,94,121,127,134,140],"fingerprint":[10],"fusion":[11],"positioning":[12],"(FFP)":[13],"method":[14,34,99],"which":[15],"combines":[16],"time-difference":[18],"of":[19,96,126,142],"arrival":[20],"(TDOA)":[21],"and":[22,73,84,113],"received":[24],"signal":[25],"strength":[26],"in":[27,82,124,133],"stationary":[29,66,88],"3D":[30,85,135],"scenarios.":[31,136],"The":[32,116],"FFP":[33],"fuses":[35],"pedestrian":[36],"dead":[37],"reckoning":[38],"(PDR)":[39],"estimation":[40],"to":[41,54],"solve":[42],"moving":[44,79],"localization":[46,102],"problem.":[47],"also":[49],"introduce":[50],"new":[51],"auxiliary":[52],"parameters":[53],"estimate":[55],"motion":[58],"state.":[59],"For":[60],"case":[62],"study,":[63],"eight":[64],"access":[65],"points":[67],"are":[68],"placed":[69],"a":[71],"bookshelf":[72],"hypermarket;":[74],"one":[75],"node":[77],"is":[78],"inside":[80],"hypermarkets":[81],"2D":[83],"scenarios":[86],"or":[87],"bookshelf.":[91],"compare":[93],"performance":[95],"our":[97,143],"proposed":[98,117],"with":[100],"existing":[101],"algorithms":[103],"such":[104],"as":[105],"k-nearest":[106,109],"neighbor,":[107,110],"weighted":[108],"pure":[111],"TDOA":[112],"Bayesian":[114],"frameworks.":[115],"outperforms":[119],"obviously":[120],"counterpart":[122],"methodologies":[123],"terms":[125],"root":[128],"mean":[129],"square":[130],"error,":[131],"especially":[132],"Simulation":[137],"results":[138],"corroborate":[139],"effectiveness":[141],"approach.":[144]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
