{"id":"https://openalex.org/W4404179891","doi":"https://doi.org/10.1109/tvt.2024.3493453","title":"Fast Subspace and DOA Estimation Method for the Case of High-Dimensional and Small Samples","display_name":"Fast Subspace and DOA Estimation Method for the Case of High-Dimensional and Small Samples","publication_year":2024,"publication_date":"2024-11-08","ids":{"openalex":"https://openalex.org/W4404179891","doi":"https://doi.org/10.1109/tvt.2024.3493453"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2024.3493453","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2024.3493453","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":null,"display_name":"Xuejun Zhang","orcid":"https://orcid.org/0000-0003-3495-3777"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xuejun Zhang","raw_affiliation_strings":["National Laboratory of Radar Signal Processing, Xidian University, Xi&#x0027;an, China"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi&#x0027;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Dazheng Feng","orcid":"https://orcid.org/0000-0002-0168-8340"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dazheng Feng","raw_affiliation_strings":["National Laboratory of Radar Signal Processing, Xidian University, Xi&#x0027;an, China"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi&#x0027;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108050907","display_name":"Wei Xing Zheng","orcid":"https://orcid.org/0000-0002-0572-5938"},"institutions":[{"id":"https://openalex.org/I63525965","display_name":"Western Sydney University","ror":"https://ror.org/03t52dk35","country_code":"AU","type":"education","lineage":["https://openalex.org/I63525965"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Weixing Zheng","raw_affiliation_strings":["School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I63525965"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19328295,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"74","issue":"3","first_page":"3958","last_page":"3975"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9851999878883362,"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"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9851999878883362,"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"}},{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9684000015258789,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T14158","display_name":"Optical Systems and Laser Technology","score":0.9327999949455261,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.7088446021080017},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.44981908798217773},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4351498782634735},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.3527248501777649},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2925688624382019},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2527918517589569}],"concepts":[{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.7088446021080017},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44981908798217773},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4351498782634735},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.3527248501777649},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2925688624382019},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2527918517589569}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvt.2024.3493453","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2024.3493453","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/G2414318839","display_name":null,"funder_award_id":"61971470","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G540253948","display_name":null,"funder_award_id":"61971349","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1995145517","https://openalex.org/W2013438974","https://openalex.org/W2044541911","https://openalex.org/W2064894286","https://openalex.org/W2096710051","https://openalex.org/W2113638573","https://openalex.org/W2115426661","https://openalex.org/W2529895811","https://openalex.org/W2587063199","https://openalex.org/W2951805303","https://openalex.org/W2978977033","https://openalex.org/W3000768990","https://openalex.org/W3030199831","https://openalex.org/W3104260746","https://openalex.org/W3178439060","https://openalex.org/W3207020834","https://openalex.org/W3207913750","https://openalex.org/W4301621763","https://openalex.org/W4312591224","https://openalex.org/W4383220262","https://openalex.org/W4388692044","https://openalex.org/W4390075126","https://openalex.org/W4396877681","https://openalex.org/W4399118890","https://openalex.org/W4399125960","https://openalex.org/W4399409956","https://openalex.org/W4399728455"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W1980381208","https://openalex.org/W2051487156","https://openalex.org/W2364594919","https://openalex.org/W2167092671","https://openalex.org/W2073681303","https://openalex.org/W1861706286","https://openalex.org/W2390279801"],"abstract_inverted_index":{"It":[0],"is":[1,51,61,85,108],"well-known":[2],"that":[3,189],"classical":[4],"direction":[5],"of":[6,16,29,49,59,80,95,119,135,174,205,225,232],"arrival":[7],"(DOA)":[8],"estimation":[9,194],"methods":[10,21,130,228],"work":[11],"well":[12],"in":[13,26,35,77,87,229],"the":[14,27,57,66,78,92,105,114,133,141,153,158,165,172,185,190,200,206,218,226,230],"case":[15,28,79,231],"large":[17,36,41],"samples.":[18,126,236],"However,":[19],"these":[20],"may":[22],"be":[23],"theoretically":[24],"invalid":[25],"small":[30,83,125,235],"samples,":[31,97],"which":[32],"frequently":[33],"occur":[34],"array":[37,42],"systems.":[38],"Such":[39],"a":[40,69,148],"has":[43],"two":[44,128],"effects:":[45],"i)":[46],"The":[47],"number":[48,134],"samples":[50,60,84],"relatively":[52],"quite":[53],"small,":[54],"and":[55,82,116,121,144,152,170,221,234],"ii)":[56],"dimension":[58],"very":[62],"large.":[63],"To":[64],"handle":[65],"above":[67],"problems,":[68],"more":[70,168],"appropriate":[71],"method":[72],"for":[73,131],"solving":[74],"DOA":[75],"estimators":[76],"high-dimensional":[81,207,233],"proposed":[86,227],"this":[88],"paper.":[89],"First,":[90],"considering":[91],"special":[93],"structure":[94],"received":[96],"an":[98],"alternative":[99],"well-estimated":[100],"second-order":[101],"statistic,":[102],"known":[103],"as":[104],"Gram":[106,142,159,202],"matrix,":[107],"originally":[109],"constructed":[110],"to":[111,163,198,216],"better":[112],"utilize":[113],"spatial":[115],"statistical":[117],"information":[118],"signals":[120],"noise":[122],"contained":[123],"by":[124,139,178],"Second,":[127],"novel":[129,149],"estimating":[132],"targets":[136,175],"are":[137,161,176,214],"derived":[138],"combining":[140],"matrix":[143,160,203],"information-theoretic":[145],"criteria.":[146],"Third,":[147],"object":[150],"function":[151],"corresponding":[154],"algorithm":[155,195],"based":[156],"on":[157],"designed":[162],"estimate":[164],"signal":[166,180,192],"subspace":[167,193],"efficiently,":[169],"then":[171],"DOAs":[173],"obtained":[177],"multiple":[179],"classification":[181],"methods.":[182],"In":[183],"particular,":[184],"theoretical":[186],"analysis":[187],"indicates":[188],"improved":[191],"only":[196],"needs":[197],"decompose":[199],"low-dimensional":[201],"instead":[204],"sample":[208],"covariance":[209],"matrix.":[210],"Finally,":[211],"simulation":[212],"results":[213],"provided":[215],"demonstrate":[217],"high":[219],"accuracy":[220],"lower":[222],"computational":[223],"complexity":[224]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
