{"id":"https://openalex.org/W3197736654","doi":"https://doi.org/10.1109/ipin51156.2021.9662577","title":"Measuring Uncertainty in Signal Fingerprinting with Gaussian Processes Going Deep","display_name":"Measuring Uncertainty in Signal Fingerprinting with Gaussian Processes Going Deep","publication_year":2021,"publication_date":"2021-11-29","ids":{"openalex":"https://openalex.org/W3197736654","doi":"https://doi.org/10.1109/ipin51156.2021.9662577","mag":"3197736654"},"language":"en","primary_location":{"id":"doi:10.1109/ipin51156.2021.9662577","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipin51156.2021.9662577","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2109.04360","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077283974","display_name":"Ran Guan","orcid":"https://orcid.org/0000-0003-1981-4648"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ran Guan","raw_affiliation_strings":["Riemann Laboratory"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Riemann Laboratory","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077911588","display_name":"Andi Zhang","orcid":"https://orcid.org/0009-0007-4855-5442"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]},{"id":"https://openalex.org/I4210159102","display_name":"Huawei Technologies (Sweden)","ror":"https://ror.org/0500fyd17","country_code":"SE","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210159102"]}],"countries":["GB","SE"],"is_corresponding":false,"raw_author_name":"Andi Zhang","raw_affiliation_strings":["Department of Computer Science and Technology, University of Cambridge","Noah\u2019s Ark Laboratory 2012 Laboratories, Huawei","Noah's Ark Laboratory 2012 Laboratories, Huawei"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, University of Cambridge","institution_ids":["https://openalex.org/I241749"]},{"raw_affiliation_string":"Noah\u2019s Ark Laboratory 2012 Laboratories, Huawei","institution_ids":["https://openalex.org/I4210159102"]},{"raw_affiliation_string":"Noah's Ark Laboratory 2012 Laboratories, Huawei","institution_ids":["https://openalex.org/I4210159102"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043652487","display_name":"Mengchao Li","orcid":"https://orcid.org/0009-0004-8707-3006"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mengchao Li","raw_affiliation_strings":["Riemann Laboratory"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Riemann Laboratory","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100431058","display_name":"Yongliang Wang","orcid":"https://orcid.org/0000-0003-0393-0513"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yongliang Wang","raw_affiliation_strings":["Riemann Laboratory"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Riemann Laboratory","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6884,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.68992787,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"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":0.9995999932289124,"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":0.9995999932289124,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9976999759674072,"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"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9940999746322632,"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/signal","display_name":"SIGNAL (programming language)","score":0.7000926733016968},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6775240302085876},{"id":"https://openalex.org/keywords/fingerprint","display_name":"Fingerprint (computing)","score":0.6471351385116577},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6397393941879272},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6004754900932312},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5897731184959412},{"id":"https://openalex.org/keywords/fingerprint-recognition","display_name":"Fingerprint recognition","score":0.5280559659004211},{"id":"https://openalex.org/keywords/measurement-uncertainty","display_name":"Measurement uncertainty","score":0.44225820899009705},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4400307238101959},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4099857807159424},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.404884934425354},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39063817262649536},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3894365429878235},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2122175097465515},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17175832390785217},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09126952290534973}],"concepts":[{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.7000926733016968},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6775240302085876},{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.6471351385116577},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6397393941879272},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6004754900932312},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5897731184959412},{"id":"https://openalex.org/C168406668","wikidata":"https://www.wikidata.org/wiki/Q178022","display_name":"Fingerprint recognition","level":3,"score":0.5280559659004211},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.44225820899009705},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4400307238101959},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4099857807159424},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.404884934425354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39063817262649536},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3894365429878235},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2122175097465515},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17175832390785217},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09126952290534973},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ipin51156.2021.9662577","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipin51156.2021.9662577","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2109.04360","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2109.04360","pdf_url":"https://arxiv.org/pdf/2109.04360","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2109.04360","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2109.04360","pdf_url":"https://arxiv.org/pdf/2109.04360","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W66306528","https://openalex.org/W1746819321","https://openalex.org/W1790231888","https://openalex.org/W1866206747","https://openalex.org/W1894025508","https://openalex.org/W2055825262","https://openalex.org/W2127600800","https://openalex.org/W2149764047","https://openalex.org/W2161798487","https://openalex.org/W2187471809","https://openalex.org/W2257113116","https://openalex.org/W2278572312","https://openalex.org/W2289104696","https://openalex.org/W2417850440","https://openalex.org/W2462826356","https://openalex.org/W2478181550","https://openalex.org/W2492689404","https://openalex.org/W2551826414","https://openalex.org/W2607595839","https://openalex.org/W2611631089","https://openalex.org/W2891224819","https://openalex.org/W2900974975","https://openalex.org/W2903415508","https://openalex.org/W2904340010","https://openalex.org/W2916031314","https://openalex.org/W2953263857","https://openalex.org/W2963711523","https://openalex.org/W3027934792","https://openalex.org/W4211049957","https://openalex.org/W6602631663","https://openalex.org/W6639216784","https://openalex.org/W6679216312","https://openalex.org/W6692054155","https://openalex.org/W6757305143"],"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":{"In":[0],"indoor":[1],"positioning,":[2],"signal":[3,9,21,47,73],"fluctuation":[4],"is":[5,11,75],"highly":[6],"location-dependent.":[7],"However,":[8],"uncertainty":[10,71],"one":[12],"critical":[13],"yet":[14],"commonly":[15,29],"overlooked":[16],"dimension":[17],"of":[18,42],"the":[19,28,40,65],"radio":[20],"to":[22,45,63],"be":[23],"fingerprinted.":[24],"This":[25,50],"paper":[26,51],"reviews":[27],"used":[30],"Gaussian":[31,55],"Processes":[32,56],"(GP)":[33],"for":[34],"probabilistic":[35],"positioning":[36],"and":[37,79],"points":[38],"out":[39],"pitfall":[41],"using":[43],"GP":[44],"model":[46],"fingerprint":[48],"uncertainty.":[49],"also":[52],"proposes":[53],"Deep":[54],"(DGP)":[57],"as":[58],"a":[59],"more":[60],"informative":[61],"alternative":[62],"address":[64],"issue.":[66],"How":[67],"DGP":[68],"better":[69],"measures":[70],"in":[72],"fingerprinting":[74],"evaluated":[76],"via":[77],"simulated":[78],"realistically":[80],"collected":[81],"datasets.":[82]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2021-09-13T00:00:00"}
