{"id":"https://openalex.org/W2993165567","doi":"https://doi.org/10.1109/lsp.2019.2957673","title":"Model-Aided Deep Neural Network for Source Number Detection","display_name":"Model-Aided Deep Neural Network for Source Number Detection","publication_year":2019,"publication_date":"2019-12-04","ids":{"openalex":"https://openalex.org/W2993165567","doi":"https://doi.org/10.1109/lsp.2019.2957673","mag":"2993165567"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2019.2957673","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2019.2957673","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1909.13273","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yuwen Yang","orcid":"https://orcid.org/0000-0001-8367-0939"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuwen Yang","raw_affiliation_strings":["Institute for Artificial Intelligence Tsinghua University, State Key Lab of Intelligent Technologies and Systems, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute for Artificial Intelligence Tsinghua University, State Key Lab of Intelligent Technologies and Systems, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Feifei Gao","orcid":"https://orcid.org/0000-0001-8896-352X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feifei Gao","raw_affiliation_strings":["Institute for Artificial Intelligence Tsinghua University, State Key Lab of Intelligent Technologies and Systems, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute for Artificial Intelligence Tsinghua University, State Key Lab of Intelligent Technologies and Systems, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Cheng Qian","orcid":"https://orcid.org/0000-0003-2249-4681"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cheng Qian","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"last","author":{"id":null,"display_name":"Guisheng Liao","orcid":null},"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":"Guisheng Liao","raw_affiliation_strings":["National Laboratory of Radar Signal Processing, Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi\u2019an, China"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":4.0072,"has_fulltext":false,"cited_by_count":58,"citation_normalized_percentile":{"value":0.94844796,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"27","issue":null,"first_page":"91","last_page":"95"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.46720001101493835,"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/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.46720001101493835,"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/T10860","display_name":"Speech and Audio Processing","score":0.23360000550746918,"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.06379999965429306,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.7164999842643738},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.6535000205039978},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.5659999847412109},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5519000291824341},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5239999890327454},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.44209998846054077},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.4212000072002411},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.40470001101493835}],"concepts":[{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.7164999842643738},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.6535000205039978},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6121000051498413},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.5659999847412109},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5519000291824341},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5389999747276306},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5239999890327454},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4805000126361847},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.44209998846054077},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.4212000072002411},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.40470001101493835},{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.4041000008583069},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.3619999885559082},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.35010001063346863},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3346000015735626},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3230000138282776},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2946000099182129},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28619998693466187},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2833999991416931},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.28060001134872437},{"id":"https://openalex.org/C2776864781","wikidata":"https://www.wikidata.org/wiki/Q52617913","display_name":"Source separation","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.25609999895095825}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/lsp.2019.2957673","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2019.2957673","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1909.13273","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.13273","pdf_url":"https://arxiv.org/pdf/1909.13273","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1909.13273","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.13273","pdf_url":"https://arxiv.org/pdf/1909.13273","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3762958806","display_name":null,"funder_award_id":"L182042","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G5896791982","display_name":null,"funder_award_id":"61771274","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8114187719","display_name":null,"funder_award_id":"61831013","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8616498718","display_name":null,"funder_award_id":"61531011","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G930939680","display_name":null,"funder_award_id":"4182030","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1966950301","https://openalex.org/W1994477821","https://openalex.org/W2033332370","https://openalex.org/W2089510574","https://openalex.org/W2096710051","https://openalex.org/W2109958193","https://openalex.org/W2113638573","https://openalex.org/W2123226256","https://openalex.org/W2128056385","https://openalex.org/W2152687999","https://openalex.org/W2155421090","https://openalex.org/W2166426208","https://openalex.org/W2321087379","https://openalex.org/W2784061726","https://openalex.org/W2886374543","https://openalex.org/W2930880287","https://openalex.org/W2938113616","https://openalex.org/W2964121960","https://openalex.org/W2967981090","https://openalex.org/W4256183008","https://openalex.org/W6631190155"],"related_works":[],"abstract_inverted_index":{"Source":[0],"number":[1,29,75,89],"detection":[2],"is":[3,32,38],"a":[4,64,69],"critical":[5],"problem":[6],"in":[7],"array":[8],"signal":[9,83],"processing.":[10],"Conventional":[11],"model-driven":[12],"methods":[13],"e.g.,":[14,116],"Akaikes":[15],"information":[16],"criterion":[17],"and":[18,68,86,95,104,133],"minimum":[19],"description":[20],"length,":[21],"suffer":[22],"from":[23],"severe":[24],"performance":[25],"degradation":[26],"when":[27],"the":[28,35,45,53,78,81,87,93,96,99,117,128,136],"of":[30,80,98,131],"samples":[31],"small":[33],"or":[34],"signal-to-noise":[36],"ratio":[37],"low.":[39],"In":[40],"this":[41],"letter,":[42],"we":[43,57],"exploit":[44],"model-aided":[46],"based":[47,61],"deep":[48],"neural":[49],"network":[50,66,71],"to":[51,110,126],"estimate":[52],"source":[54,74,88],"number.":[55],"Specifically,":[56],"propose":[58],"two":[59],"eigenvalue":[60],"networks,":[62,100],"i.e.,":[63],"regression":[65],"(ERNet)":[67],"classification":[70],"(ECNet),":[72],"for":[73],"detection,":[76],"where":[77],"eigenvalues":[79],"received":[82],"covariance":[84],"matrix":[85],"are":[90,124],"used":[91],"as":[92],"input":[94],"label":[97],"respectively.":[101],"Furthermore,":[102],"ERNet":[103,132],"ECNet":[105,134],"can":[106],"be":[107],"easily":[108],"generalized":[109],"handle":[111],"coherent":[112],"sources":[113],"by":[114],"adopting,":[115],"forward-backward":[118],"spatial":[119],"smoothing":[120],"technique.":[121],"Numerical":[122],"results":[123],"included":[125],"showcase":[127],"remarkable":[129],"improvements":[130],"over":[135],"existing":[137],"methods.":[138]},"counts_by_year":[{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":5}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2019-12-13T00:00:00"}