{"id":"https://openalex.org/W2782742784","doi":"https://doi.org/10.1109/glocom.2017.8254556","title":"Geomagnetic Field Based Indoor Localization Using Recurrent Neural Networks","display_name":"Geomagnetic Field Based Indoor Localization Using Recurrent Neural Networks","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2782742784","doi":"https://doi.org/10.1109/glocom.2017.8254556","mag":"2782742784"},"language":"en","primary_location":{"id":"doi:10.1109/glocom.2017.8254556","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2017.8254556","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2017 - 2017 IEEE Global Communications Conference","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/A5102124546","display_name":"Ho Jun Jang","orcid":null},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Ho Jun Jang","raw_affiliation_strings":["School of Electrical Engineering, Korea University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Korea University, Seoul, Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101598285","display_name":"Jae\u2013Min Shin","orcid":"https://orcid.org/0000-0002-2090-9612"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jae Min Shin","raw_affiliation_strings":["School of Electrical Engineering, Korea University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Korea University, Seoul, Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103209090","display_name":"Lynn Choi","orcid":"https://orcid.org/0000-0001-5254-9116"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Lynn Choi","raw_affiliation_strings":["School of Electrical Engineering, Korea University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Korea University, Seoul, Korea","institution_ids":["https://openalex.org/I197347611"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102124546"],"corresponding_institution_ids":["https://openalex.org/I197347611"],"apc_list":null,"apc_paid":null,"fwci":4.2194,"has_fulltext":false,"cited_by_count":67,"citation_normalized_percentile":{"value":0.94776313,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"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/T10860","display_name":"Speech and Audio Processing","score":0.992900013923645,"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/T11192","display_name":"Underwater Vehicles and Communication Systems","score":0.9914000034332275,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/earths-magnetic-field","display_name":"Earth's magnetic field","score":0.8792130947113037},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7067141532897949},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5644804239273071},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5403661131858826},{"id":"https://openalex.org/keywords/magnetometer","display_name":"Magnetometer","score":0.5180742740631104},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4996798038482666},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.491432785987854},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4810512363910675},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.45564162731170654},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.339995801448822},{"id":"https://openalex.org/keywords/magnetic-field","display_name":"Magnetic field","score":0.29576337337493896},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1470850706100464},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09156683087348938}],"concepts":[{"id":"https://openalex.org/C199635899","wikidata":"https://www.wikidata.org/wiki/Q6500960","display_name":"Earth's magnetic field","level":3,"score":0.8792130947113037},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7067141532897949},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5644804239273071},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5403661131858826},{"id":"https://openalex.org/C153946474","wikidata":"https://www.wikidata.org/wiki/Q333921","display_name":"Magnetometer","level":3,"score":0.5180742740631104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4996798038482666},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.491432785987854},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4810512363910675},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.45564162731170654},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.339995801448822},{"id":"https://openalex.org/C115260700","wikidata":"https://www.wikidata.org/wiki/Q11408","display_name":"Magnetic field","level":2,"score":0.29576337337493896},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1470850706100464},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09156683087348938},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/glocom.2017.8254556","is_oa":false,"landing_page_url":"https://doi.org/10.1109/glocom.2017.8254556","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2017 - 2017 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5099999904632568,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2646853","https://openalex.org/W1542766183","https://openalex.org/W1836465849","https://openalex.org/W1947853012","https://openalex.org/W2018589614","https://openalex.org/W2054799039","https://openalex.org/W2064675550","https://openalex.org/W2067040941","https://openalex.org/W2096157747","https://openalex.org/W2100989187","https://openalex.org/W2132110947","https://openalex.org/W2142136129","https://openalex.org/W2143243918","https://openalex.org/W2148868231","https://openalex.org/W2157457404","https://openalex.org/W2163993204","https://openalex.org/W2170102584","https://openalex.org/W2171357383","https://openalex.org/W2331632140","https://openalex.org/W2566140095","https://openalex.org/W2915125769","https://openalex.org/W2999214452","https://openalex.org/W6638667902","https://openalex.org/W6681301362"],"related_works":["https://openalex.org/W2074940407","https://openalex.org/W2035596186","https://openalex.org/W2885295284","https://openalex.org/W2398658113","https://openalex.org/W309521798","https://openalex.org/W1502875874","https://openalex.org/W1905009105","https://openalex.org/W2138929496","https://openalex.org/W4302027234","https://openalex.org/W1529619320"],"abstract_inverted_index":{"The":[0,125],"time-varying,":[1],"unstable":[2],"nature":[3],"of":[4,11,22,82,97,147,157,188,207,229,238,256,264,267,288],"RF":[5,40],"signals":[6],"has":[7],"limited":[8],"the":[9,44,56,66,73,80,83,120,136,145,158,179,185,213,230,239,261,276,286,297],"accuracy":[10,278],"RF-based":[12,74,298],"indoor":[13,30,61,75,100,109],"positioning":[14,254],"techniques":[15],"such":[16,32],"as":[17,33,117,141,182,184,223],"Wi-Fi":[18],"fingerprinting.":[19,299],"Positioning":[20],"errors":[21],"over":[23,161],"10":[24],"meters":[25,258,266],"are":[26,232,241,281],"reported":[27],"in":[28,52],"large-scale":[29],"environment":[31],"airport":[34],"and":[35,88,236],"department":[36],"stores.":[37],"Compared":[38],"to":[39,119,153,260,284],"or":[41],"ultrasound":[42],"signals,":[43],"geomagnetic":[45,58,85,194],"field":[46,59,86,139,149,195],"signal":[47,50],"exhibits":[48],"stable":[49],"strength":[51],"time":[53],"domain.":[54],"However,":[55],"existing":[57],"based":[60,99,177],"localization":[62,110,244,277],"still":[63],"relies":[64],"on":[65,178,197],"fingerprinting":[67,270],"technique,":[68],"which":[69,292],"is":[70,127],"borrowed":[71],"from":[72,212],"positioning.":[76],"This":[77],"cannot":[78,273],"resolve":[79],"distribution":[81],"same":[84,137],"values":[87,150],"thus":[89],"became":[90],"a":[91,107,142,154,165,174,193,204,224],"major":[92],"reason":[93],"for":[94,234,243],"diminished":[95],"performance":[96],"geomagnetic-":[98],"localization.":[101],"In":[102,246],"this":[103,247],"paper,":[104],"we":[105,202,250,280],"propose":[106],"novel":[108],"technique":[111],"that":[112,128],"uses":[113],"magnetometer":[114],"sensor":[115,159],"readings":[116,160],"input":[118,181],"artificial":[121],"neural":[122,167],"network":[123,168],"models.":[124],"idea":[126],"although":[129],"there":[130],"can":[131,172],"be":[132],"multiple":[133],"locations":[134],"having":[135],"magnetic":[138,148],"value,":[140],"pedestrian":[143,209],"moves":[144],"sequence":[146,187],"will":[151],"lead":[152],"unique":[155],"pattern":[156],"time.":[162],"We":[163,190,215,272],"use":[164,216],"recurrent":[166],"(RNN)":[169],"since":[170],"it":[171],"characterize":[173],"particular":[175],"location":[176],"current":[180],"well":[183],"past":[186],"inputs.":[189],"first":[191],"build":[192],"map":[196],"our":[198,268],"campus":[199],"test-bed.":[200],"Then,":[201],"generate":[203],"million":[205],"traces":[206,231,240],"various":[208],"walking":[210],"patterns":[211],"map.":[214],"Google":[217],"Tensorflow":[218],"with":[219,296],"NVIDIA":[220],"cuDNN":[221],"library":[222],"Deep":[225],"Learning":[226],"framework.":[227],"95%":[228],"used":[233,242],"training":[235],"5%":[237],"evaluation.":[245],"preliminary":[248],"evaluation,":[249],"show":[251],"an":[252],"average":[253,262],"error":[255,263],"1.062":[257],"compared":[259],"3.14":[265],"BLE":[269],"results.":[271],"only":[274],"improve":[275],"but":[279],"also":[282],"able":[283],"address":[285],"problem":[287],"continuous":[289],"route":[290],"tracking,":[291],"was":[293],"not":[294],"possible":[295]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":13},{"year":2018,"cited_by_count":4}],"updated_date":"2026-03-17T09:09:15.849793","created_date":"2025-10-10T00:00:00"}
