{"id":"https://openalex.org/W2901968357","doi":"https://doi.org/10.1109/ipin.2018.8533852","title":"Neural Network Based Radio Fingerprint Similarity Measure","display_name":"Neural Network Based Radio Fingerprint Similarity Measure","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2901968357","doi":"https://doi.org/10.1109/ipin.2018.8533852","mag":"2901968357"},"language":"en","primary_location":{"id":"doi:10.1109/ipin.2018.8533852","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipin.2018.8533852","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)","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/A5030157801","display_name":"Boxian Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Boxian Dong","raw_affiliation_strings":["Research Dept., Indoo.rs GmbH, Vienna, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Dept., Indoo.rs GmbH, Vienna, Austria","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062388550","display_name":"T. Burgess","orcid":"https://orcid.org/0000-0002-6993-5918"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thomas Burgess","raw_affiliation_strings":["Research Dept., Indoo.rs GmbH, Vienna, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Dept., Indoo.rs GmbH, Vienna, Austria","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014964205","display_name":"Hans-Berndt Neuner","orcid":"https://orcid.org/0000-0002-2846-1402"},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Hans-Berndt Neuner","raw_affiliation_strings":["Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria","institution_ids":["https://openalex.org/I145847075"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075908787","display_name":"Susanne Fercher","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Susanne Fercher","raw_affiliation_strings":["Research Dept., Indoo.rs GmbH, Vienna, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Dept., Indoo.rs GmbH, Vienna, Austria","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"8"},"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":0.9936000108718872,"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":0.9757999777793884,"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/jaccard-index","display_name":"Jaccard index","score":0.9506831169128418},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.7522984743118286},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6982328295707703},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6566810607910156},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5824295878410339},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5231436491012573},{"id":"https://openalex.org/keywords/distance-measures","display_name":"Distance measures","score":0.5113767981529236},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5042463541030884},{"id":"https://openalex.org/keywords/fingerprint","display_name":"Fingerprint (computing)","score":0.47980502247810364},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4785255193710327},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.47034552693367004},{"id":"https://openalex.org/keywords/signal-strength","display_name":"Signal strength","score":0.4185256361961365},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3339231014251709},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27232152223587036},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.12030437588691711},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09547460079193115}],"concepts":[{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.9506831169128418},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.7522984743118286},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6982328295707703},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6566810607910156},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5824295878410339},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5231436491012573},{"id":"https://openalex.org/C2639959","wikidata":"https://www.wikidata.org/wiki/Q1344778","display_name":"Distance measures","level":2,"score":0.5113767981529236},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5042463541030884},{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.47980502247810364},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4785255193710327},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.47034552693367004},{"id":"https://openalex.org/C176808163","wikidata":"https://www.wikidata.org/wiki/Q17105794","display_name":"Signal strength","level":3,"score":0.4185256361961365},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3339231014251709},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27232152223587036},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.12030437588691711},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09547460079193115},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ipin.2018.8533852","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipin.2018.8533852","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)","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":25,"referenced_works":["https://openalex.org/W1497337727","https://openalex.org/W1893431417","https://openalex.org/W2001298596","https://openalex.org/W2005266231","https://openalex.org/W2011224697","https://openalex.org/W2041076788","https://openalex.org/W2046128567","https://openalex.org/W2099817569","https://openalex.org/W2110176871","https://openalex.org/W2117934391","https://openalex.org/W2122915435","https://openalex.org/W2142870898","https://openalex.org/W2160266360","https://openalex.org/W2207534450","https://openalex.org/W2244101296","https://openalex.org/W2269319342","https://openalex.org/W2278572312","https://openalex.org/W2343107556","https://openalex.org/W2353025630","https://openalex.org/W2531361785","https://openalex.org/W2563417884","https://openalex.org/W2699555409","https://openalex.org/W3092980197","https://openalex.org/W6705614895","https://openalex.org/W6730500219"],"related_works":["https://openalex.org/W2129888254","https://openalex.org/W2115336194","https://openalex.org/W4294624291","https://openalex.org/W1563946824","https://openalex.org/W2809929944","https://openalex.org/W4389080932","https://openalex.org/W2571943156","https://openalex.org/W2767543488","https://openalex.org/W4387233853","https://openalex.org/W2901968357"],"abstract_inverted_index":{"The":[0,95,213],"radio":[1],"signal":[2],"Received":[3],"Signal":[4],"Strength":[5],"Indicator":[6],"(RSSI)":[7],"based":[8,57,102,145,170,191],"localization":[9,19],"method":[10],"is":[11],"one":[12],"of":[13,107,119],"the":[14,23,35,80,116,124,132,142,148,158,167,188,203,210,220,230],"most":[15,149],"often":[16],"adopted":[17],"indoors":[18],"methods,":[20],"due":[21],"to":[22,50,78,89,164],"fact":[24],"that":[25,184],"it":[26,180],"can":[27,172,181],"be":[28],"used":[29],"by":[30,110],"any":[31,38],"handhold":[32],"devices":[33],"on":[34,58],"market":[36],"without":[37],"modification.":[39],"This":[40,62],"paper":[41],"will":[42],"present":[43],"a":[44,99,175],"new":[45],"deep":[46],"learning":[47],"inspired":[48],"model":[49,63,103,146,171],"predict":[51],"locational":[52,90,232],"distance/similarity":[53],"between":[54],"two":[55,159],"points":[56,161,183],"their":[59],"RSSI":[60,88,100],"measurement.":[61],"uses":[64],"carefully":[65],"designed":[66],"input":[67],"features,":[68],"neural":[69],"network":[70],"architecture,":[71],"as":[72,74,216],"well":[73],"purposely":[75],"crafted":[76,87],"pre-training":[77],"combine":[79],"features":[81,222],"and":[82,115,140,152,199,228],"statistics":[83],"from":[84,223],"multiple":[85],"hand":[86],"similarity":[91],"models":[92,97,135,227],"(reference":[93],"models).":[94],"reference":[96,134,226],"include":[98],"difference":[101,118],"(euclidean":[104],"distance),":[105],"number":[106],"visible":[108,121],"signals":[109,122],"both":[111],"measurements":[112,126],"(Jaccard":[113],"distance)":[114],"rank":[117],"commonly":[120],"in":[123,209],"comparing":[125],"(Spearman's":[127],"footrule).":[128],"Our":[129],"evaluation":[130],"shows":[131],"three":[133,225],"have":[136],"very":[137,176,206],"distinct":[138,182],"strengths":[139],"weaknesses:":[141],"euclidean":[143],"distance":[144,169,205,233],"generates":[147,229],"detailed":[150],"prediction":[151],"has":[153,193],"best":[154,221,231],"estimation":[155],"results":[156],"when":[157],"measurement":[160],"are":[162,185],"close":[163],"each":[165],"other;":[166],"Jaccard":[168],"only":[173],"provide":[174],"coarse":[177,197],"estimation,":[178,198],"however,":[179],"far":[186],"away;":[187],"Spearman's":[189],"footrule":[190],"solution":[192],"overall":[194],"good":[195],"but":[196],"its":[200],"estimations":[201],"represent":[202],"relative":[204],"well,":[207],"especially":[208],"middle":[211],"range.":[212],"proposed":[214],"method,":[215],"we":[217],"expected,":[218],"combines":[219],"all":[224],"estimation.":[234]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}