{"id":"https://openalex.org/W4417438659","doi":"https://doi.org/10.1109/jiot.2025.3645627","title":"Splitting-Mean Model-Based Robust Range Estimation Under Mixed LOS/NLOS Conditions","display_name":"Splitting-Mean Model-Based Robust Range Estimation Under Mixed LOS/NLOS Conditions","publication_year":2025,"publication_date":"2025-12-17","ids":{"openalex":"https://openalex.org/W4417438659","doi":"https://doi.org/10.1109/jiot.2025.3645627"},"language":null,"primary_location":{"id":"doi:10.1109/jiot.2025.3645627","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2025.3645627","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Internet of Things Journal","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/A5023394161","display_name":"Chee\u2010Hyun Park","orcid":"https://orcid.org/0000-0002-9739-5277"},"institutions":[{"id":"https://openalex.org/I4575257","display_name":"Hanyang University","ror":"https://ror.org/046865y68","country_code":"KR","type":"education","lineage":["https://openalex.org/I4575257"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Chee-Hyun Park","raw_affiliation_strings":["School of Electronic Engineering, Hanyang University, Seoul, South Korea","School of Electronic Engineering, Hanyang University, Seoul, Korea"],"raw_orcid":"https://orcid.org/0000-0002-9739-5277","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Hanyang University, Seoul, South Korea","institution_ids":["https://openalex.org/I4575257"]},{"raw_affiliation_string":"School of Electronic Engineering, Hanyang University, Seoul, Korea","institution_ids":["https://openalex.org/I4575257"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5023394161"],"corresponding_institution_ids":["https://openalex.org/I4575257"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34263139,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"5","first_page":"9731","last_page":"9746"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.7178999781608582,"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":0.7178999781608582,"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":0.04899999871850014,"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/T10860","display_name":"Speech and Audio Processing","score":0.03480000048875809,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.7566999793052673},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5536999702453613},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.4666000008583069},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.45190000534057617},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.44279998540878296},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.4399999976158142},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.43380001187324524},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4311999976634979},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.41029998660087585}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7566999793052673},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5536999702453613},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5519999861717224},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46869999170303345},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.4666000008583069},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46129998564720154},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.45190000534057617},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.44279998540878296},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.4399999976158142},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.43380001187324524},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4311999976634979},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.41029998660087585},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.40369999408721924},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.3799000084400177},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C90652560","wikidata":"https://www.wikidata.org/wiki/Q11091747","display_name":"Minimum mean square error","level":3,"score":0.36090001463890076},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.3538999855518341},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3452000021934509},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3334999978542328},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3255999982357025},{"id":"https://openalex.org/C71907059","wikidata":"https://www.wikidata.org/wiki/Q223323","display_name":"Root mean square","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.273499995470047},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.2662000060081482},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.25929999351501465},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C48548287","wikidata":"https://www.wikidata.org/wiki/Q6803557","display_name":"Mean-shift","level":3,"score":0.2572000026702881},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2025.3645627","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2025.3645627","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"range":[3,27],"estimation":[4,28,49],"algorithms":[5],"specifically":[6],"designed":[7],"for":[8,110],"environments":[9],"that":[10,119],"exhibit":[11],"line-of-sight":[12],"(LOS)":[13],"and":[14,45,74],"non-line-of-sight":[15],"(NLOS)":[16],"conditions.":[17],"Leveraging":[18],"a":[19,30,37,46,81,105],"splitting":[20,33,52],"mean":[21,34,53,65,87],"model,":[22],"we":[23],"propose":[24],"three":[25,121],"distinct":[26],"techniques:":[29],"closed-form":[31],"two-stage":[32,51],"(CTSM)":[35],"method,":[36],"robust":[38],"Gaussian":[39],"mixture":[40],"model-expectation":[41],"maximization":[42],"(RGMM-EM)":[43],"algorithm":[44],"kernel":[47],"density":[48],"(KDE)-based":[50],"(KTSM)":[54],"method.":[55],"These":[56],"methods":[57,122],"assign":[58],"weights":[59],"inversely":[60],"proportional":[61],"to":[62],"the":[63,85,93,97,111],"estimated":[64],"squared":[66,75,88],"error":[67,72,89],"(MSE),":[68],"thereby":[69],"minimizing":[70],"both":[71],"variance":[73],"bias":[76],"simultaneously.":[77],"We":[78],"further":[79],"present":[80],"theoretical":[82],"analysis":[83],"of":[84,128],"root":[86],"(RMSE)":[90],"associated":[91],"with":[92],"proposed":[94],"methods.":[95],"Additionally,":[96],"Cram\u00e9r-Rao":[98],"lower":[99],"bound":[100],"(CRLB)":[101],"is":[102],"derived":[103],"using":[104],"one-hot":[106],"encoding":[107],"framework":[108],"tailored":[109],"mixed":[112,134],"LOS/NLOS":[113,135],"noise":[114],"model.":[115],"Simulation":[116],"results":[117],"demonstrate":[118],"all":[120],"outperform":[123],"conventional":[124],"techniques":[125],"in":[126,133],"terms":[127],"RMSE,":[129],"confirming":[130],"their":[131],"effectiveness":[132],"environments.":[136]},"counts_by_year":[],"updated_date":"2026-02-21T06:11:54.161237","created_date":"2025-12-17T00:00:00"}