{"id":"https://openalex.org/W4399118890","doi":"https://doi.org/10.1109/tvt.2024.3406709","title":"Robust Total Maximum Versoria Algorithm for Efficient DOA Estimation in Noisy Inputs","display_name":"Robust Total Maximum Versoria Algorithm for Efficient DOA Estimation in Noisy Inputs","publication_year":2024,"publication_date":"2024-05-29","ids":{"openalex":"https://openalex.org/W4399118890","doi":"https://doi.org/10.1109/tvt.2024.3406709"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2024.3406709","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2024.3406709","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Transactions on Vehicular Technology","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/A5072884907","display_name":"Omer M. Abdelrhman","orcid":null},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Omer M. Abdelrhman","raw_affiliation_strings":["Information Science and Technology College, Dalian Maritime University, Dalian, China"],"raw_orcid":"https://orcid.org/0000-0002-2082-2437","affiliations":[{"raw_affiliation_string":"Information Science and Technology College, Dalian Maritime University, Dalian, China","institution_ids":["https://openalex.org/I43313876"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103276902","display_name":"Sen Li","orcid":"https://orcid.org/0000-0001-6422-1089"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sen Li","raw_affiliation_strings":["Information Science and Technology College, Dalian Maritime University, Dalian, China"],"raw_orcid":"https://orcid.org/0000-0001-6422-1089","affiliations":[{"raw_affiliation_string":"Information Science and Technology College, Dalian Maritime University, Dalian, China","institution_ids":["https://openalex.org/I43313876"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071168965","display_name":"Yuzi Dou","orcid":"https://orcid.org/0000-0003-1726-6270"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuzi Dou","raw_affiliation_strings":["Information Science and Technology College, Dalian Maritime University, Dalian, China"],"raw_orcid":"https://orcid.org/0000-0003-1726-6270","affiliations":[{"raw_affiliation_string":"Information Science and Technology College, Dalian Maritime University, Dalian, China","institution_ids":["https://openalex.org/I43313876"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088968438","display_name":"Bin Lin","orcid":"https://orcid.org/0000-0002-6125-9839"},"institutions":[{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Bin","raw_affiliation_strings":["Information Science and Technology College, Dalian Maritime University, Dalian, China"],"raw_orcid":"https://orcid.org/0000-0002-6125-9839","affiliations":[{"raw_affiliation_string":"Information Science and Technology College, Dalian Maritime University, Dalian, China","institution_ids":["https://openalex.org/I43313876"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I43313876"],"apc_list":null,"apc_paid":null,"fwci":3.0563,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.92211301,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"73","issue":"10","first_page":"15087","last_page":"15097"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9767000079154968,"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"}},"topics":[{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9767000079154968,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9735999703407288,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9575999975204468,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/algorithm","display_name":"Algorithm","score":0.5520551204681396},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5158430933952332}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5520551204681396},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5158430933952332}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvt.2024.3406709","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2024.3406709","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Transactions on Vehicular Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G310282618","display_name":null,"funder_award_id":"62371085","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3560047313","display_name":null,"funder_award_id":"XLYC2002078","funder_id":"https://openalex.org/F4320329895","funder_display_name":"Liaoning Revitalization Talents Program"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320329895","display_name":"Liaoning Revitalization Talents Program","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1972485395","https://openalex.org/W1996124770","https://openalex.org/W2007336860","https://openalex.org/W2008339846","https://openalex.org/W2015881065","https://openalex.org/W2043746281","https://openalex.org/W2099873122","https://openalex.org/W2123224804","https://openalex.org/W2138668867","https://openalex.org/W2149755721","https://openalex.org/W2149817982","https://openalex.org/W2152987686","https://openalex.org/W2153605253","https://openalex.org/W2187618798","https://openalex.org/W2342174554","https://openalex.org/W2403797236","https://openalex.org/W2588195252","https://openalex.org/W2595155976","https://openalex.org/W2624726413","https://openalex.org/W2892433162","https://openalex.org/W2950727611","https://openalex.org/W2981737415","https://openalex.org/W3024853581","https://openalex.org/W3032339198","https://openalex.org/W3033200111","https://openalex.org/W3082509552","https://openalex.org/W3125949113","https://openalex.org/W3190927513","https://openalex.org/W3191141184","https://openalex.org/W4211118645","https://openalex.org/W4230367971","https://openalex.org/W4243947880","https://openalex.org/W4292968459","https://openalex.org/W4296126585","https://openalex.org/W4296334398","https://openalex.org/W4312361655","https://openalex.org/W4312550567","https://openalex.org/W4312605226","https://openalex.org/W4312646110","https://openalex.org/W4313144084","https://openalex.org/W4321195191","https://openalex.org/W4323817660","https://openalex.org/W4366147547","https://openalex.org/W4367316823","https://openalex.org/W4376851446","https://openalex.org/W4382364533","https://openalex.org/W4383503632"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2051487156","https://openalex.org/W2073681303","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"In":[0],"adaptive":[1,62],"filtering":[2],"for":[3,37,42,60],"direction-of-arrival":[4],"(DOA)":[5],"estimation,":[6],"traditional":[7],"bias-compensated":[8],"models":[9],"often":[10],"suffer":[11],"from":[12],"significant":[13],"performance":[14],"degradation":[15],"in":[16,145,162],"the":[17,51,69,79,82,100,107,112,118,130,152],"presence":[18],"of":[19,81,129,154,164],"impulsive":[20,46,146],"noise":[21,147],"affecting":[22],"both":[23],"input":[24],"and":[25,123,136,166],"output":[26],"signals.":[27],"This":[28],"paper":[29],"addresses":[30],"this":[31],"challenge":[32],"by":[33,45],"introducing":[34],"new":[35],"algorithms":[36],"robust":[38,61,83],"DOA":[39,63],"estimation":[40,64],"tailored":[41],"environments":[43],"contaminated":[44],"noise.":[47],"We":[48],"initially":[49],"employ":[50],"error-in-variable":[52],"(EIV)":[53],"model":[54],"to":[55,116],"derive":[56],"a":[57,75,95],"generalized":[58],"framework":[59,102],"algorithms.":[65],"Our":[66],"methodology":[67],"incorporates":[68],"Maximum":[70],"Versoria":[71],"(MV)":[72],"criterion":[73],"as":[74],"cost":[76],"function,":[77],"facilitating":[78],"development":[80],"Gradient":[84],"Total":[85],"MV":[86],"(GTMV)":[87],"algorithm.":[88],"To":[89],"further":[90],"enhance":[91],"performance,":[92],"we":[93],"introduce":[94],"variable":[96],"step-size":[97],"mechanism":[98],"within":[99],"GTMV":[101,131],"(VSS-GTMV),":[103],"which":[104],"dynamically":[105],"adjusts":[106],"step":[108],"size":[109],"based":[110],"on":[111],"signal-to-noise":[113],"ratio":[114],"(SNR)":[115],"optimize":[117],"trade-off":[119],"between":[120],"convergence":[121,127],"speed":[122],"steady-state":[124],"error.":[125],"The":[126],"properties":[128],"algorithm":[132],"are":[133],"analytically":[134],"examined,":[135],"its":[137],"effectiveness":[138],"is":[139],"substantiated":[140],"through":[141],"extensive":[142],"numerical":[143],"simulations":[144],"environments.":[148],"Comparative":[149],"results":[150],"demonstrate":[151],"superiority":[153],"our":[155],"proposed":[156],"methods":[157],"over":[158],"existing":[159],"competitive":[160],"approaches":[161],"terms":[163],"accuracy":[165],"robustness.":[167]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}