{"id":"https://openalex.org/W3033032209","doi":"https://doi.org/10.1109/lcomm.2020.2999904","title":"UWB NLOS/LOS Classification Using Deep Learning Method","display_name":"UWB NLOS/LOS Classification Using Deep Learning Method","publication_year":2020,"publication_date":"2020-06-04","ids":{"openalex":"https://openalex.org/W3033032209","doi":"https://doi.org/10.1109/lcomm.2020.2999904","mag":"3033032209"},"language":"en","primary_location":{"id":"doi:10.1109/lcomm.2020.2999904","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lcomm.2020.2999904","pdf_url":null,"source":{"id":"https://openalex.org/S147316732","display_name":"IEEE Communications Letters","issn_l":"1089-7798","issn":["1089-7798","1558-2558","2373-7891"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316002","host_organization_name":"IEEE Communications Society","host_organization_lineage":["https://openalex.org/P4310316002","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Communications Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Communications Letters","raw_type":"journal-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/A5074175188","display_name":"Changhui Jiang","orcid":"https://orcid.org/0000-0002-4788-2464"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changhui Jiang","raw_affiliation_strings":["School of Automation, Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-4788-2464","affiliations":[{"raw_affiliation_string":"School of Automation, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108714718","display_name":"Jichun Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jichun Shen","raw_affiliation_strings":["Hesai Technology, Building L2-B, Hongqiao World Centre, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hesai Technology, Building L2-B, Hongqiao World Centre, Shanghai, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100396688","display_name":"Shuai Chen","orcid":"https://orcid.org/0000-0002-8562-5784"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Chen","raw_affiliation_strings":["School of Automation, Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100374346","display_name":"Yuwei Chen","orcid":"https://orcid.org/0000-0003-0148-3609"},"institutions":[{"id":"https://openalex.org/I33876163","display_name":"Finnish Geospatial Research Institute","ror":"https://ror.org/01zv3gf04","country_code":"FI","type":"facility","lineage":["https://openalex.org/I33876163"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Yuwei Chen","raw_affiliation_strings":["Department of Photogrammetry and Remote Sensing, Finnish Geospatial Research Institute, Finland"],"raw_orcid":"https://orcid.org/0000-0003-0148-3609","affiliations":[{"raw_affiliation_string":"Department of Photogrammetry and Remote Sensing, Finnish Geospatial Research Institute, Finland","institution_ids":["https://openalex.org/I33876163"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102005820","display_name":"Di Liu","orcid":"https://orcid.org/0000-0003-4528-9605"},"institutions":[{"id":"https://openalex.org/I2799736854","display_name":"Nanjing Institute of Technology","ror":"https://ror.org/00n6txq60","country_code":"CN","type":"education","lineage":["https://openalex.org/I2799736854"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di Liu","raw_affiliation_strings":["School of Automation, Nanjing Institute of Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-4528-9605","affiliations":[{"raw_affiliation_string":"School of Automation, Nanjing Institute of Technology, Nanjing, China","institution_ids":["https://openalex.org/I2799736854"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102714100","display_name":"Yuming Bo","orcid":"https://orcid.org/0000-0002-0228-6749"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuming Bo","raw_affiliation_strings":["School of Automation, Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":11.0627,"has_fulltext":false,"cited_by_count":280,"citation_normalized_percentile":{"value":0.99137489,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"24","issue":"10","first_page":"2226","last_page":"2230"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12024","display_name":"Ultra-Wideband Communications Technology","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/T12024","display_name":"Ultra-Wideband Communications Technology","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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9912999868392944,"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/T10655","display_name":"GNSS positioning and interference","score":0.9793000221252441,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/non-line-of-sight-propagation","display_name":"Non-line-of-sight propagation","score":0.7966153025627136},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7075990438461304},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.562921404838562},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5508602857589722},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37223440408706665},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34773868322372437},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.33313173055648804},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.2241668999195099}],"concepts":[{"id":"https://openalex.org/C154910267","wikidata":"https://www.wikidata.org/wiki/Q1740982","display_name":"Non-line-of-sight propagation","level":3,"score":0.7966153025627136},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7075990438461304},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.562921404838562},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5508602857589722},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37223440408706665},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34773868322372437},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.33313173055648804},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.2241668999195099}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lcomm.2020.2999904","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lcomm.2020.2999904","pdf_url":null,"source":{"id":"https://openalex.org/S147316732","display_name":"IEEE Communications Letters","issn_l":"1089-7798","issn":["1089-7798","1558-2558","2373-7891"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310316002","host_organization_name":"IEEE Communications Society","host_organization_lineage":["https://openalex.org/P4310316002","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Communications Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Communications Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G1552320267","display_name":null,"funder_award_id":"61601225","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2008363523","https://openalex.org/W2060873147","https://openalex.org/W2075744751","https://openalex.org/W2123220865","https://openalex.org/W2162718622","https://openalex.org/W2735562740","https://openalex.org/W2790826789","https://openalex.org/W2794396553","https://openalex.org/W2896938208","https://openalex.org/W2901240584","https://openalex.org/W2920151315","https://openalex.org/W2966035132"],"related_works":["https://openalex.org/W2172272784","https://openalex.org/W2003817535","https://openalex.org/W4307436769","https://openalex.org/W4323793210","https://openalex.org/W2366306259","https://openalex.org/W3101720559","https://openalex.org/W2143447014","https://openalex.org/W2218045119","https://openalex.org/W3172283447","https://openalex.org/W4380075502"],"abstract_inverted_index":{"Ultra-Wide-Band":[0],"(UWB)":[1],"was":[2,70,146,157,164,205],"recognized":[3],"as":[4,96],"its":[5],"great":[6],"potential":[7],"in":[8,43,131,148,195],"constructing":[9],"accurate":[10],"indoor":[11,16],"position":[12,53],"system":[13],"(IPS).":[14],"However,":[15],"environments":[17],"were":[18,94,178,193],"full":[19],"of":[20,100,200],"complex":[21],"objects,":[22],"the":[23,29,33,37,60,64,74,83,97,101,149,161,170,175,181,196,209],"signals":[24],"might":[25],"be":[26],"reflected":[27],"by":[28,117],"obstacles.":[30],"Compared":[31],"with":[32,133,202],"Line-Of-Sight":[34],"(LOS)":[35],"signal,":[36],"signal":[38,46,92,152],"transmitting":[39],"path":[40],"delay":[41],"contained":[42],"None-Line-Of-Sight":[44],"(NLOS)":[45],"would":[47],"induce":[48],"positive":[49],"distance":[50],"errors":[51],"and":[52,122,168,173],"errors.":[54],"Before":[55],"employing":[56],"ranging":[57],"information":[58],"from":[59,82,189],"channels":[61],"to":[62,160],"calculate":[63],"position,":[65],"LOS/NLOS":[66],"classification":[67,219],"or":[68,89],"identification":[69],"necessary":[71],"for":[72,166,183,207],"selecting":[73],"\u201cclean\u201d":[75],"channels.":[76],"In":[77,138],"conventional":[78],"method,":[79],"features":[80,171],"extracted":[81],"UWB":[84,150,154],"channel":[85],"impulse":[86],"response":[87],"(CIR)":[88],"some":[90],"other":[91],"properties":[93],"employed":[95,147,165,194],"input":[98,159],"vector":[99],"machine":[102],"learning":[103,114,143],"methods,":[104],"e.g.":[105],"Support":[106],"Vector":[107],"Machine":[108],"(SVM),":[109],"Multi-layer":[110],"Perception":[111],"(MLP).":[112],"Deep":[113],"methods":[115],"represented":[116],"Convolutional":[118],"neural":[119],"network":[120],"(CNN)":[121],"Long":[123],"Short-Term":[124],"Memory":[125],"(LSTM)":[126],"had":[127],"performed":[128],"superior":[129],"performance":[130],"dealing":[132],"time":[134],"series":[135],"data":[136,156],"classification.":[137,153,184],"this":[139],"pap":[140],"er,":[141],"deep":[142],"method":[144],"CNN-LSTM":[145,201,215],"NLOS/LOS":[151],"CIR":[155],"directly":[158],"CNN-LSTM.":[162],"CNN":[163,176],"exploring":[167],"extracting":[169],"automatically,":[172],"then,":[174],"outputs":[177],"fed":[179],"into":[180],"LSTM":[182],"Open":[185],"source":[186],"datasets":[187],"collected":[188],"seven":[190],"different":[191,203],"sites":[192],"experiments.":[197],"Classification":[198],"accuracy":[199],"settings":[204],"compared":[206],"analyzing":[208],"performance.":[210,220],"The":[211],"results":[212],"showed":[213],"that":[214],"obtained":[216],"stat":[217],"e-of-art":[218]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":72},{"year":2024,"cited_by_count":60},{"year":2023,"cited_by_count":65},{"year":2022,"cited_by_count":39},{"year":2021,"cited_by_count":25},{"year":2020,"cited_by_count":7}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
