{"id":"https://openalex.org/W4392397506","doi":"https://doi.org/10.1109/lgrs.2024.3372707","title":"Complex-Valued Neural Network With Multistep Training for Single-Snapshot DOA Estimation","display_name":"Complex-Valued Neural Network With Multistep Training for Single-Snapshot DOA Estimation","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4392397506","doi":"https://doi.org/10.1109/lgrs.2024.3372707"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2024.3372707","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2024.3372707","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing 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/A5066034534","display_name":"Yuheng Zhang","orcid":"https://orcid.org/0009-0003-0215-7108"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuheng Zhang","raw_affiliation_strings":["School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101853864","display_name":"Rong Zeng","orcid":"https://orcid.org/0000-0003-3835-5467"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rong Zeng","raw_affiliation_strings":["School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102896267","display_name":"Shuchen Zhang","orcid":"https://orcid.org/0009-0004-9294-1250"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuchen Zhang","raw_affiliation_strings":["School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059735499","display_name":"Jiachen Wang","orcid":"https://orcid.org/0009-0007-6183-1157"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiachen Wang","raw_affiliation_strings":["School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048127007","display_name":"Yifeng Wu","orcid":"https://orcid.org/0000-0002-7714-7620"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifeng Wu","raw_affiliation_strings":["School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5066034534"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":1.1251,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.74349502,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"21","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9990000128746033,"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"}},"topics":[{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9990000128746033,"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/T11698","display_name":"Underwater Acoustics Research","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.998199999332428,"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/snapshot","display_name":"Snapshot (computer storage)","score":0.7737538814544678},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.697212815284729},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6278237700462341},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4745498597621918},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4596526026725769},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3691636323928833},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3448878526687622},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33474022150039673}],"concepts":[{"id":"https://openalex.org/C55282118","wikidata":"https://www.wikidata.org/wiki/Q252683","display_name":"Snapshot (computer storage)","level":2,"score":0.7737538814544678},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.697212815284729},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6278237700462341},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4745498597621918},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4596526026725769},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3691636323928833},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3448878526687622},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33474022150039673},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2024.3372707","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2024.3372707","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1752422785","display_name":null,"funder_award_id":"62371446","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2293978782","display_name":null,"funder_award_id":"KQTD20190929172704911","funder_id":"https://openalex.org/F4320336569","funder_display_name":"Shenzhen Science and Technology Innovation Program"},{"id":"https://openalex.org/G3429214797","display_name":null,"funder_award_id":"62101603","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6937380381","display_name":null,"funder_award_id":"62201623","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"},{"id":"https://openalex.org/F4320336569","display_name":"Shenzhen Science and Technology Innovation Program","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1774526428","https://openalex.org/W2103519107","https://openalex.org/W2128659236","https://openalex.org/W2357251224","https://openalex.org/W2581566261","https://openalex.org/W2861848708","https://openalex.org/W2973301417","https://openalex.org/W3163282512","https://openalex.org/W3188870776","https://openalex.org/W3206953857","https://openalex.org/W4206727442","https://openalex.org/W4284965389","https://openalex.org/W4319303143"],"related_works":["https://openalex.org/W2542847180","https://openalex.org/W3034994054","https://openalex.org/W2805712290","https://openalex.org/W2155226960","https://openalex.org/W2548022288","https://openalex.org/W2909129499","https://openalex.org/W2392087771","https://openalex.org/W2761598930","https://openalex.org/W230091440","https://openalex.org/W2553994394"],"abstract_inverted_index":{"Estimating":[0],"the":[1,33,41,45,60,82,92,96,102,130,140,144],"direction":[2],"of":[3,113,150],"arrival":[4],"(DOA)":[5],"for":[6,22,71,105],"single":[7,23],"snapshot":[8,24],"signals":[9,70],"is":[10,87],"paramount":[11],"in":[12,153],"practical":[13],"applications.":[14],"In":[15],"this":[16],"letter,":[17],"we":[18],"present":[19],"a":[20,29,111,148],"method":[21,146],"DOA":[25,106],"estimation":[26,37],"based":[27],"on":[28],"neural":[30],"network,":[31],"namely":[32],"complex-valued":[34,68],"residual":[35],"angle":[36],"network":[38,43,131],"(CVRAEN).":[39],"Firstly,":[40],"CVRAEN":[42],"utilizes":[44],"initial":[46],"feature":[47,69,75,84],"extraction":[48,85],"module":[49,86,98],"including":[50,116],"complex":[51,55],"linear":[52],"layer":[53,57],"and":[54,63,100,123],"convolutional":[56],"to":[58,78,89,127,159],"process":[59,91],"input":[61],"signal":[62,74],"decompose":[64],"it":[65],"into":[66],"multiple":[67],"obtaining":[72],"preliminary":[73],"extraction.":[76],"Subsequently,":[77],"extract":[79],"depth":[80,83],"features,":[81],"employed":[88],"further":[90],"extracted":[93,103],"features.":[94],"Finally,":[95],"mapping":[97],"combines":[99],"maps":[101],"features":[104],"estimation.":[107],"This":[108],"letter":[109],"employs":[110],"series":[112],"training":[114],"steps,":[115],"pre-training,":[117],"signal-to-noise":[118],"ratio":[119],"(SNR)":[120],"stratified":[121],"training,":[122,126],"SNR":[124,142],"adaptive":[125],"continuously":[128],"optimize":[129],"model.":[132],"The":[133],"main":[134],"simulation":[135],"results":[136],"indicate":[137],"that,":[138],"within":[139],"simulated":[141],"range,":[143],"proposed":[145],"exhibits":[147],"decrease":[149],"approximately":[151],"30%":[152],"root":[154],"mean":[155],"square":[156],"error":[157],"compared":[158],"traditional":[160],"methods.":[161]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}