{"id":"https://openalex.org/W4387010757","doi":"https://doi.org/10.1109/tim.2023.3318716","title":"Two-Dimensional Off-Grid DOA Estimation With Metasurface Aperture Based on MMV Sparse Bayesian Learning","display_name":"Two-Dimensional Off-Grid DOA Estimation With Metasurface Aperture Based on MMV Sparse Bayesian Learning","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4387010757","doi":"https://doi.org/10.1109/tim.2023.3318716"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3318716","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":true,"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":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-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/A5075235448","display_name":"Haosheng Fu","orcid":"https://orcid.org/0000-0002-8885-8807"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"funder","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haosheng Fu","raw_affiliation_strings":["National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036145098","display_name":"Fengzhou Dai","orcid":"https://orcid.org/0000-0003-2166-2516"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"funder","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fengzhou Dai","raw_affiliation_strings":["National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031338939","display_name":"Ling Hong","orcid":"https://orcid.org/0000-0002-6710-4264"},"institutions":[{"id":"https://openalex.org/I88830068","display_name":"Shaanxi Normal University","ror":"https://ror.org/0170z8493","country_code":"CN","type":"funder","lineage":["https://openalex.org/I88830068"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Hong","raw_affiliation_strings":["School of Computer Science, Shaanxi Normal University, Xi’an, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Shaanxi Normal University, Xi’an, China","institution_ids":["https://openalex.org/I88830068"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.624,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.732037,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":82,"max":86},"biblio":{"volume":"72","issue":null,"first_page":"1","last_page":"18"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9997,"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"}},"topics":[{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9997,"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"}},{"id":"https://openalex.org/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9996,"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9982,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/interpolation","display_name":"Interpolation","score":0.6313242},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.55215013},{"id":"https://openalex.org/keywords/aperture","display_name":"Aperture (computer memory)","score":0.47496197}],"concepts":[{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.6825452},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6593368},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.6313242},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5939734},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.55215013},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5072805},{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.47571236},{"id":"https://openalex.org/C78336883","wikidata":"https://www.wikidata.org/wiki/Q4779385","display_name":"Aperture (computer memory)","level":2,"score":0.47496197},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4647094},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.44031626},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2869626},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24731952},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18483001},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.11227718},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08504048},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3318716","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":true,"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":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[{"funder":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China","award_id":"62271363"}],"datasets":[],"versions":[],"referenced_works_count":45,"referenced_works":["https://openalex.org/W1596369052","https://openalex.org/W1933412717","https://openalex.org/W1947282229","https://openalex.org/W1982686915","https://openalex.org/W1999170002","https://openalex.org/W2016625067","https://openalex.org/W2028823365","https://openalex.org/W2064191652","https://openalex.org/W2066218102","https://openalex.org/W2066632311","https://openalex.org/W2067480525","https://openalex.org/W2078511883","https://openalex.org/W2087494601","https://openalex.org/W2113524221","https://openalex.org/W2113638573","https://openalex.org/W2129638195","https://openalex.org/W2141529198","https://openalex.org/W2163762086","https://openalex.org/W2169326247","https://openalex.org/W2597051817","https://openalex.org/W2605368703","https://openalex.org/W2897535970","https://openalex.org/W2964278319","https://openalex.org/W2969618511","https://openalex.org/W2983012712","https://openalex.org/W3016391799","https://openalex.org/W3098157702","https://openalex.org/W3098523046","https://openalex.org/W3102166889","https://openalex.org/W3119315185","https://openalex.org/W3141595720","https://openalex.org/W3158234426","https://openalex.org/W3159400923","https://openalex.org/W3193636205","https://openalex.org/W3200354748","https://openalex.org/W3213693699","https://openalex.org/W4206116476","https://openalex.org/W4225935974","https://openalex.org/W4226437694","https://openalex.org/W4250955649","https://openalex.org/W4285119360","https://openalex.org/W4286656204","https://openalex.org/W4320001371","https://openalex.org/W4383960488","https://openalex.org/W4385269112"],"related_works":["https://openalex.org/W946352265","https://openalex.org/W4234142113","https://openalex.org/W3145389907","https://openalex.org/W3104199760","https://openalex.org/W3020787026","https://openalex.org/W2370926798","https://openalex.org/W2364741597","https://openalex.org/W2334479858","https://openalex.org/W1971388572","https://openalex.org/W1492103595"],"abstract_inverted_index":{"Metasurface":[0],"aperture-based":[1],"systems":[2],"(MABSs)":[3],"have":[4,22],"recently":[5],"attracted":[6],"more":[7,220],"attention":[8],"due":[9],"to":[10,145,158],"their":[11],"simple":[12],"hardware":[13],"structure,":[14],"easy":[15],"fabrication,":[16],"and":[17,20,88,186,202,227,236],"low-cost":[18],"measurement,":[19],"they":[21],"been":[23],"developed":[24],"for":[25,45,125,213],"the":[26,38,48,56,78,82,97,102,135,151,162,169,175,190,193,200,207,215,224,244],"direction":[27],"of":[28,58,81,177,223,239,247],"arrival":[29],"(DOA)":[30],"estimation.":[31],"In":[32],"this":[33],"paper,":[34],"we":[35,116],"focus":[36],"on":[37,134],"two-dimensional":[39],"(2D)":[40],"off-grid":[41,121,140,152,225],"DOA":[42,122,141,209],"estimation":[43,123,142,210],"problem":[44,75],"MABSs.":[46,248],"Unlike":[47],"traditional":[49],"multi-channel":[50],"array":[51],"measurement":[52,79,179,245],"mechanism,":[53],"MABSs":[54,126],"retrieve":[55],"directional":[57],"incident":[59],"sources":[60],"by":[61,92,106,167,199],"generating":[62],"multiple":[63,178],"spatially":[64],"incoherent":[65],"quasi-random":[66],"radiation":[67],"patterns.":[68],"Meanwhile,":[69],"it":[70],"will":[71],"bring":[72],"a":[73,85,119,128,138],"new":[74],"in":[76,161],"that":[77],"matrix":[80,87],"MABS":[83],"is":[84,148],"pseudo-random":[86],"cannot":[89,109],"be":[90,110,159],"described":[91],"mathematical":[93],"formulas.":[94],"Besides,":[95],"under":[96,174],"compressed":[98],"sensing":[99],"(CS)":[100],"framework,":[101],"grid":[103],"mismatch":[104],"caused":[105],"discretization":[107],"operation":[108],"ignored.":[111],"To":[112],"handle":[113],"these":[114],"problems,":[115],"innovatively":[117],"propose":[118],"2D":[120,139],"model":[124],"using":[127,168,192],"Gaussian":[129,194],"interpolation":[130,195],"kernel.":[131,196],"Then,":[132],"based":[133],"proposed":[136,216],"model,":[137,163],"method,":[143],"referred":[144],"as":[146,156],"2D-OGGISBL,":[147],"developed.":[149],"Specifically,":[150],"errors":[153,226],"are":[154,165],"considered":[155],"parameters":[157],"calibrated":[160],"which":[164,241],"estimated":[166],"expectation":[170],"maximization":[171],"(EM)":[172],"algorithm":[173],"framework":[176],"vector":[180],"(MMV)":[181],"sparse":[182],"Bayesian":[183],"learning":[184],"(SBL)":[185],"eliminated":[187],"via":[188],"updating":[189],"grids":[191],"As":[197],"shown":[198],"simulation":[201],"experimental":[203],"results,":[204],"compared":[205],"with":[206],"existing":[208],"methods":[211],"used":[212],"MABSs,":[214],"method":[217],"can":[218],"calibrate":[219],"than":[221],"95%":[222],"works":[228],"well":[229],"even":[230],"at":[231],"low":[232],"SNR":[233],"(5":[234],"dB)":[235],"few":[237],"numbers":[238],"snapshots,":[240],"significantly":[242],"improves":[243],"performance":[246]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4387010757","counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2025-03-26T01:30:48.821826","created_date":"2023-09-26"}