{"id":"https://openalex.org/W2587652998","doi":"https://doi.org/10.1109/lsp.2018.2848838","title":"Joint DOA and Frequency Estimation With Sub-Nyquist Sampling in the Sparse Array System","display_name":"Joint DOA and Frequency Estimation With Sub-Nyquist Sampling in the Sparse Array System","publication_year":2018,"publication_date":"2018-06-18","ids":{"openalex":"https://openalex.org/W2587652998","doi":"https://doi.org/10.1109/lsp.2018.2848838","mag":"2587652998"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2018.2848838","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2018.2848838","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/1702.01386","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Liang Liu","orcid":"https://orcid.org/0000-0003-2033-2507"},"institutions":[{"id":"https://openalex.org/I2800372957","display_name":"China Electronics Technology Group Corporation","ror":"https://ror.org/0098hst83","country_code":"CN","type":"company","lineage":["https://openalex.org/I2800372957"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Liu","raw_affiliation_strings":["10th Institute of China Electronics Technology Group Corporation, China"],"raw_orcid":"https://orcid.org/0000-0003-2033-2507","affiliations":[{"raw_affiliation_string":"10th Institute of China Electronics Technology Group Corporation, China","institution_ids":["https://openalex.org/I2800372957"]}]},{"author_position":"last","author":{"id":null,"display_name":"Ping Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Wei","raw_affiliation_strings":["Center for Cyber Security, School of Information and Communication Engineering, University of Electronic Science and Technology of China, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Cyber Security, School of Information and Communication Engineering, University of Electronic Science and Technology of China, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6627,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.84481523,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"25","issue":"9","first_page":"1285","last_page":"1289"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.6644999980926514,"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":0.6644999980926514,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.14659999310970306,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10891","display_name":"Radar Systems and Signal Processing","score":0.08879999816417694,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/joint","display_name":"Joint (building)","score":0.6901000142097473},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5879999995231628},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.579800009727478},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.48590001463890076},{"id":"https://openalex.org/keywords/vectorization","display_name":"Vectorization (mathematics)","score":0.47600001096725464},{"id":"https://openalex.org/keywords/degrees-of-freedom","display_name":"Degrees of freedom (physics and chemistry)","score":0.4343999922275543},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.427700012922287},{"id":"https://openalex.org/keywords/sensor-array","display_name":"Sensor array","score":0.3993000090122223},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.3603000044822693}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7009000182151794},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.6901000142097473},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6021000146865845},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5879999995231628},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.579800009727478},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.48590001463890076},{"id":"https://openalex.org/C41681595","wikidata":"https://www.wikidata.org/wiki/Q7917855","display_name":"Vectorization (mathematics)","level":2,"score":0.47600001096725464},{"id":"https://openalex.org/C208081375","wikidata":"https://www.wikidata.org/wiki/Q274502","display_name":"Degrees of freedom (physics and chemistry)","level":2,"score":0.4343999922275543},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.427700012922287},{"id":"https://openalex.org/C66251956","wikidata":"https://www.wikidata.org/wiki/Q7451086","display_name":"Sensor array","level":2,"score":0.3993000090122223},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.3603000044822693},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.35429999232292175},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3379000127315521},{"id":"https://openalex.org/C145177509","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse array","level":2,"score":0.3158000111579895},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.29750001430511475},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2759999930858612},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2676999866962433},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.265500009059906},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/lsp.2018.2848838","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2018.2848838","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:1702.01386","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1702.01386","pdf_url":"https://arxiv.org/pdf/1702.01386","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:1702.01386","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1702.01386","pdf_url":"https://arxiv.org/pdf/1702.01386","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":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1964521756","https://openalex.org/W2043625269","https://openalex.org/W2057503509","https://openalex.org/W2071707134","https://openalex.org/W2078204800","https://openalex.org/W2098174516","https://openalex.org/W2101840010","https://openalex.org/W2114129195","https://openalex.org/W2346852820","https://openalex.org/W2357400875","https://openalex.org/W2519899344","https://openalex.org/W2522694309","https://openalex.org/W2963237514","https://openalex.org/W2964011032","https://openalex.org/W6634697270"],"related_works":[],"abstract_inverted_index":{"Several":[0],"array":[1],"systems":[2],"along":[3],"with":[4,38,92],"algorithms":[5],"based":[6],"on":[7,53],"sub-Nyquist":[8,39,50],"sampling":[9,90],"techniques":[10],"have":[11],"been":[12],"extensively":[13],"studied.":[14],"This":[15],"letter":[16],"is":[17,62],"committed":[18],"to":[19],"the":[20,47,66,73,81,96,111,119],"joint":[21,82],"frequency":[22],"and":[23,71,116,118],"direction-of-arrival":[24],"estimation":[25,83,102,120],"of":[26,69,75,98,114],"more":[27],"sources":[28],"than":[29],"sensors":[30,94,115],"in":[31,101],"a":[32,55,88,99,108],"subband":[33],"by":[34],"using":[35],"sparse":[36],"arrays":[37],"sampling.":[40,51],"The":[41],"newly":[42],"defined":[43],"block":[44],"vectorization":[45],"eliminates":[46],"interference":[48],"from":[49],"Based":[52],"this,":[54],"novel":[56],"augmented":[57],"sample":[58],"covariance":[59],"matrix":[60],"method":[61],"proposed,":[63],"which":[64],"enhances":[65],"spatial":[67],"degrees":[68],"freedom":[70],"increases":[72],"number":[74],"identifiable":[76],"sources.":[77],"Simulations":[78],"show":[79],"that":[80],"can":[84],"be":[85],"realized":[86],"at":[87,95],"lower":[89],"rate":[91],"fewer":[93],"expense":[97],"degradation":[100],"performance.":[103,121],"Such":[104],"an":[105],"algorithm":[106],"provides":[107],"tradeoff":[109],"between":[110],"expensive":[112],"resource":[113],"channels,":[117]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2017-02-17T00:00:00"}
