{"id":"https://openalex.org/W2154722188","doi":"https://doi.org/10.1109/tsp.2010.2098402","title":"Maximum Likelihood Direction Finding in Spatially Colored Noise Fields Using Sparse Sensor Arrays","display_name":"Maximum Likelihood Direction Finding in Spatially Colored Noise Fields Using Sparse Sensor Arrays","publication_year":2010,"publication_date":"2010-12-14","ids":{"openalex":"https://openalex.org/W2154722188","doi":"https://doi.org/10.1109/tsp.2010.2098402","mag":"2154722188"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2010.2098402","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2010.2098402","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Transactions on Signal Processing","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/A5108515500","display_name":"Tao Li","orcid":"https://orcid.org/0000-0001-6114-8461"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tao Li","raw_affiliation_strings":["Department of Electrical and Systems Engineering, Washington University of Saint Louis, Saint Louis, MO, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Systems Engineering, Washington University of Saint Louis, Saint Louis, MO, USA","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031212309","display_name":"Arye Nehorai","orcid":"https://orcid.org/0000-0002-9055-9865"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arye Nehorai","raw_affiliation_strings":["Department of Electrical and Systems Engineering, Washington University of Saint Louis, Saint Louis, MO, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Systems Engineering, Washington University of Saint Louis, Saint Louis, MO, USA","institution_ids":["https://openalex.org/I204465549"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5108515500"],"corresponding_institution_ids":["https://openalex.org/I204465549"],"apc_list":null,"apc_paid":null,"fwci":2.023,"has_fulltext":false,"cited_by_count":45,"citation_normalized_percentile":{"value":0.87745575,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"59","issue":"3","first_page":"1048","last_page":"1062"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9983000159263611,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9972000122070312,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.8105224370956421},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5920597910881042},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5848652124404907},{"id":"https://openalex.org/keywords/direction-of-arrival","display_name":"Direction of arrival","score":0.5721247792243958},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.571416974067688},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.5654552578926086},{"id":"https://openalex.org/keywords/cram\u00e9r\u2013rao-bound","display_name":"Cram\u00e9r\u2013Rao bound","score":0.515798807144165},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5105522871017456},{"id":"https://openalex.org/keywords/narrowband","display_name":"Narrowband","score":0.4664119482040405},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.4563887119293213},{"id":"https://openalex.org/keywords/colors-of-noise","display_name":"Colors of noise","score":0.45512670278549194},{"id":"https://openalex.org/keywords/minimum-mean-square-error","display_name":"Minimum mean square error","score":0.43859124183654785},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.43775802850723267},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.43383342027664185},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.35861077904701233},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3445701003074646},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1339472234249115},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.12006247043609619},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07370558381080627}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.8105224370956421},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5920597910881042},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5848652124404907},{"id":"https://openalex.org/C172051844","wikidata":"https://www.wikidata.org/wiki/Q5280438","display_name":"Direction of arrival","level":3,"score":0.5721247792243958},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.571416974067688},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.5654552578926086},{"id":"https://openalex.org/C4978587","wikidata":"https://www.wikidata.org/wiki/Q1138810","display_name":"Cram\u00e9r\u2013Rao bound","level":3,"score":0.515798807144165},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5105522871017456},{"id":"https://openalex.org/C2776096036","wikidata":"https://www.wikidata.org/wiki/Q1140483","display_name":"Narrowband","level":2,"score":0.4664119482040405},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.4563887119293213},{"id":"https://openalex.org/C114996537","wikidata":"https://www.wikidata.org/wiki/Q4854529","display_name":"Colors of noise","level":3,"score":0.45512670278549194},{"id":"https://openalex.org/C90652560","wikidata":"https://www.wikidata.org/wiki/Q11091747","display_name":"Minimum mean square error","level":3,"score":0.43859124183654785},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.43775802850723267},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.43383342027664185},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.35861077904701233},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3445701003074646},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1339472234249115},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.12006247043609619},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07370558381080627},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2010.2098402","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2010.2098402","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Transactions on Signal Processing","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":45,"referenced_works":["https://openalex.org/W640156484","https://openalex.org/W1004888717","https://openalex.org/W1551284826","https://openalex.org/W1834265579","https://openalex.org/W1868963500","https://openalex.org/W1899196398","https://openalex.org/W1963544757","https://openalex.org/W1967639437","https://openalex.org/W1971085322","https://openalex.org/W1989765167","https://openalex.org/W1993446440","https://openalex.org/W2023257259","https://openalex.org/W2024476015","https://openalex.org/W2031693516","https://openalex.org/W2032123328","https://openalex.org/W2049633694","https://openalex.org/W2064713618","https://openalex.org/W2084333685","https://openalex.org/W2087543940","https://openalex.org/W2096040567","https://openalex.org/W2102678064","https://openalex.org/W2103970904","https://openalex.org/W2106359513","https://openalex.org/W2106573196","https://openalex.org/W2116414176","https://openalex.org/W2121165908","https://openalex.org/W2124208643","https://openalex.org/W2128954565","https://openalex.org/W2129553513","https://openalex.org/W2130193001","https://openalex.org/W2131826810","https://openalex.org/W2132772110","https://openalex.org/W2133525817","https://openalex.org/W2145957927","https://openalex.org/W2149755721","https://openalex.org/W2152524816","https://openalex.org/W2152759373","https://openalex.org/W2158541895","https://openalex.org/W2163645146","https://openalex.org/W2166543153","https://openalex.org/W2798333393","https://openalex.org/W3150929985","https://openalex.org/W3215543355","https://openalex.org/W4245808720","https://openalex.org/W6639024257"],"related_works":["https://openalex.org/W2121830377","https://openalex.org/W3216235436","https://openalex.org/W2155167894","https://openalex.org/W2148770873","https://openalex.org/W2392120193","https://openalex.org/W1510459835","https://openalex.org/W1831233010","https://openalex.org/W2097065270","https://openalex.org/W2118537597","https://openalex.org/W2154722188"],"abstract_inverted_index":{"We":[0,37,88],"consider":[1],"the":[2,26,43,66,73,79,96,100,109,117,123,127,130],"problem":[3],"of":[4,11,20,45,60,78,132],"maximum":[5],"likelihood":[6],"(ML)":[7],"direction-of-arrival":[8],"(DOA)":[9],"estimation":[10,59],"narrowband":[12],"signals":[13,50,128],"using":[14],"sparse":[15],"sensor":[16],"arrays,":[17],"which":[18],"consist":[19],"widely":[21],"separated":[22],"subarrays":[23],"such":[24],"that":[25,82,108,122],"unknown":[27],"spatially":[28],"colored":[29],"noise":[30],"field":[31],"is":[32],"uncorrelated":[33],"between":[34],"different":[35],"subarrays.":[36],"develop":[38],"ML":[39,80,103],"DOA":[40,58,133],"estimators":[41,111],"under":[42],"assumptions":[44],"zero-mean":[46,85],"and":[47,91,99,121],"non-zero-mean":[48,61],"Gaussian":[49,62,86],"based":[51],"on":[52],"an":[53],"Expectation-Maximization":[54],"(EM)":[55],"framework.":[56],"For":[57],"signals,":[63],"we":[64],"derive":[65],"Cram\u00e9r-Rao":[67],"bound":[68],"(CRB)":[69],"as":[70,72],"well":[71],"asymptotic":[74],"error":[75],"covariance":[76],"matrix":[77],"estimator":[81],"improperly":[83],"assumes":[84],"signals.":[87],"provide":[89,113],"analytical":[90],"numerical":[92],"performance":[93],"comparisons":[94],"for":[95],"existing":[97,118],"deterministic":[98,119],"proposed":[101,110],"stochastic":[102],"estimators.":[104],"The":[105],"results":[106],"show":[107],"normally":[112],"better":[114],"accuracy":[115,131],"than":[116],"estimator,":[120],"nonzero":[124],"means":[125],"in":[126],"improve":[129],"estimation.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":5},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
