{"id":"https://openalex.org/W2897521205","doi":"https://doi.org/10.1587/transcom.2018ebp3232","title":"2-D DOA Estimation Based on Sparse Bayesian Learning for L-Shaped Nested Array","display_name":"2-D DOA Estimation Based on Sparse Bayesian Learning for L-Shaped Nested Array","publication_year":2018,"publication_date":"2018-10-22","ids":{"openalex":"https://openalex.org/W2897521205","doi":"https://doi.org/10.1587/transcom.2018ebp3232","mag":"2897521205"},"language":"en","primary_location":{"id":"doi:10.1587/transcom.2018ebp3232","is_oa":false,"landing_page_url":"https://doi.org/10.1587/transcom.2018ebp3232","pdf_url":null,"source":{"id":"https://openalex.org/S2493627025","display_name":"IEICE Transactions on Communications","issn_l":"0916-8516","issn":["0916-8516","1745-1345"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4320800604","host_organization_name":"Institute of Electronics, Information and Communication Engineers","host_organization_lineage":["https://openalex.org/P4320800604"],"host_organization_lineage_names":["Institute of Electronics, Information and Communication Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEICE Transactions on Communications","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/A5100432093","display_name":"Lu Chen","orcid":"https://orcid.org/0000-0002-5685-7017"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lu CHEN","raw_affiliation_strings":["National University of Defense Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023882700","display_name":"Daping Bi","orcid":"https://orcid.org/0000-0002-2745-4681"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Daping BI","raw_affiliation_strings":["National University of Defense Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101609099","display_name":"Jifei Pan","orcid":"https://orcid.org/0000-0002-9278-7060"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jifei PAN","raw_affiliation_strings":["National University of Defense Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11838926,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"E102.B","issue":"5","first_page":"992","last_page":"999"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9948999881744385,"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/T10860","display_name":"Speech and Audio Processing","score":0.9948999881744385,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9940000176429749,"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":0.9905999898910522,"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":0.6593819856643677},{"id":"https://openalex.org/keywords/singular-value-decomposition","display_name":"Singular value decomposition","score":0.6013292670249939},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.5964106321334839},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5790650248527527},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5478026270866394},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5192311406135559},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.5121907591819763},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4883994460105896},{"id":"https://openalex.org/keywords/direction-of-arrival","display_name":"Direction of arrival","score":0.4843519926071167},{"id":"https://openalex.org/keywords/eigendecomposition-of-a-matrix","display_name":"Eigendecomposition of a matrix","score":0.4817045331001282},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.4524362087249756},{"id":"https://openalex.org/keywords/cholesky-decomposition","display_name":"Cholesky decomposition","score":0.4335615038871765},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35835766792297363},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3313063085079193},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2392561137676239},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.16076374053955078},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.10544425249099731},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07861530780792236}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6593819856643677},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.6013292670249939},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.5964106321334839},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5790650248527527},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5478026270866394},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5192311406135559},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5121907591819763},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4883994460105896},{"id":"https://openalex.org/C172051844","wikidata":"https://www.wikidata.org/wiki/Q5280438","display_name":"Direction of arrival","level":3,"score":0.4843519926071167},{"id":"https://openalex.org/C169756996","wikidata":"https://www.wikidata.org/wiki/Q194919","display_name":"Eigendecomposition of a matrix","level":3,"score":0.4817045331001282},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.4524362087249756},{"id":"https://openalex.org/C34727166","wikidata":"https://www.wikidata.org/wiki/Q515375","display_name":"Cholesky decomposition","level":3,"score":0.4335615038871765},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35835766792297363},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3313063085079193},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2392561137676239},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.16076374053955078},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.10544425249099731},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07861530780792236},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C21822782","wikidata":"https://www.wikidata.org/wiki/Q131214","display_name":"Antenna (radio)","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1587/transcom.2018ebp3232","is_oa":false,"landing_page_url":"https://doi.org/10.1587/transcom.2018ebp3232","pdf_url":null,"source":{"id":"https://openalex.org/S2493627025","display_name":"IEICE Transactions on Communications","issn_l":"0916-8516","issn":["0916-8516","1745-1345"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4320800604","host_organization_name":"Institute of Electronics, Information and Communication Engineers","host_organization_lineage":["https://openalex.org/P4320800604"],"host_organization_lineage_names":["Institute of Electronics, Information and Communication Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEICE Transactions on Communications","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":21,"referenced_works":["https://openalex.org/W1496112662","https://openalex.org/W1561977859","https://openalex.org/W1978318660","https://openalex.org/W1985041550","https://openalex.org/W2044390680","https://openalex.org/W2070173386","https://openalex.org/W2070222581","https://openalex.org/W2086533823","https://openalex.org/W2098174516","https://openalex.org/W2113638573","https://openalex.org/W2115248952","https://openalex.org/W2116414176","https://openalex.org/W2117523044","https://openalex.org/W2127271355","https://openalex.org/W2130601302","https://openalex.org/W2160979406","https://openalex.org/W2161666352","https://openalex.org/W2288170217","https://openalex.org/W2301138634","https://openalex.org/W2430061959","https://openalex.org/W2468750340"],"related_works":["https://openalex.org/W2781473773","https://openalex.org/W3014345041","https://openalex.org/W2079690995","https://openalex.org/W290159486","https://openalex.org/W2067567045","https://openalex.org/W1592113329","https://openalex.org/W2360754927","https://openalex.org/W2948834710","https://openalex.org/W2387106590","https://openalex.org/W2150953077"],"abstract_inverted_index":{"In":[0],"sparsity-based":[1],"optimization":[2],"problems":[3],"for":[4],"two":[5,131,162],"dimensional":[6],"(2-D)":[7],"direction-of-arrival":[8],"(DOA)":[9],"estimation":[10,26,193],"using":[11,115],"L-shaped":[12],"nested":[13,57],"arrays,":[14],"one":[15],"of":[16,85,93,130,141,146],"the":[17,51,62,86,91,94,116,120,138,142,152,165,168,187,196],"major":[18],"issues":[19],"is":[20,28,48,66,78,108,124,133,149,172],"computational":[21],"complexity.":[22],"A":[23,42],"2-D":[24,169],"DOA":[25],"algorithm":[27,107,189,201],"proposed":[29,166,188],"based":[30],"on":[31],"reconsitution":[32],"sparse":[33,104],"Bayesian":[34,105],"learning":[35,106],"(RSBL)":[36],"and":[37,97,135,151,179,206],"cross":[38,127],"covariance":[39,128],"matrix":[40,77,96,123,129,145,181],"decomposition.":[41],"single":[43],"measurement":[44,64,71],"vector":[45,65,72,122,144],"(SMV)":[46],"model":[47],"obtained":[49],"by":[50,80],"difference":[52],"coarray":[53],"corresponding":[54],"to":[55,110],"one-dimensional":[56,112,117,176],"array.":[58],"Through":[59,164],"spatial":[60],"smoothing,":[61],"signal":[63,76],"transformed":[67,173],"into":[68,174],"a":[69,175,180],"multiple":[70],"(MMV)":[73],"matrix.":[74,87],"The":[75,103,126],"separated":[79],"singular":[81],"values":[82],"decomposition":[83],"(SVD)":[84],"Using":[88],"this":[89],"method,":[90],"dimensionality":[92],"sensing":[95],"data":[98],"size":[99],"can":[100,158],"be":[101,159],"reduced.":[102],"used":[109],"estimate":[111],"angles.":[113],"By":[114],"angle":[118,192],"estimations,":[119],"steering":[121,143],"reconstructed.":[125],"dimensions":[132],"decomposed":[134],"transformed.":[136],"Then":[137],"closed":[139],"expression":[140],"another":[147],"dimension":[148],"derived,":[150],"angles":[153],"are":[154],"estimated.":[155],"Automatic":[156],"pairing":[157],"achieved":[160],"in":[161],"dimensions.":[163],"algorithm,":[167],"search":[170,177],"problem":[171,178],"transformation":[182],"problem.":[183],"Simulations":[184],"show":[185],"that":[186],"has":[190],"better":[191],"accuracy":[194],"than":[195],"traditional":[197],"two-dimensional":[198],"direction":[199],"finding":[200],"at":[202],"low":[203],"signal-to-noise":[204],"ratio":[205],"few":[207],"samples.":[208]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
