{"id":"https://openalex.org/W4224919450","doi":"https://doi.org/10.1109/icassp43922.2022.9746740","title":"DOA M-Estimation Using Sparse Bayesian Learning","display_name":"DOA M-Estimation Using Sparse Bayesian Learning","publication_year":2022,"publication_date":"2022-04-27","ids":{"openalex":"https://openalex.org/W4224919450","doi":"https://doi.org/10.1109/icassp43922.2022.9746740"},"language":"en","primary_location":{"id":"doi:10.1109/icassp43922.2022.9746740","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9746740","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://research.aalto.fi/en/publications/83e02916-074d-4ef8-acef-ad14fa631ad9","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014100872","display_name":"Christoph F. Mecklenbr\u00e4uker","orcid":"https://orcid.org/0000-0001-9571-0379"},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Christoph F. Mecklenbrauker","raw_affiliation_strings":["Inst. of Telecommunications, TU Wien,Vienna,Austria","Inst. of Telecommunications, TU Wien, Vienna, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inst. of Telecommunications, TU Wien,Vienna,Austria","institution_ids":["https://openalex.org/I145847075"]},{"raw_affiliation_string":"Inst. of Telecommunications, TU Wien, Vienna, Austria","institution_ids":["https://openalex.org/I145847075"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004718675","display_name":"Peter Gerstoft","orcid":"https://orcid.org/0000-0002-0471-062X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peter Gerstoft","raw_affiliation_strings":["UCSD,NoiseLab,San Diego,CA,USA","NoiseLab, UCSD, San Diego, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UCSD,NoiseLab,San Diego,CA,USA","institution_ids":[]},{"raw_affiliation_string":"NoiseLab, UCSD, San Diego, CA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031785324","display_name":"Esa Ollila","orcid":"https://orcid.org/0000-0002-0898-5313"},"institutions":[{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Esa Ollila","raw_affiliation_strings":["Aalto University,Dept. of Signal Processing and Acoustics,Aalto,Finland","Dept. of Signal Processing and Acoustics, Aalto University, Aalto, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aalto University,Dept. of Signal Processing and Acoustics,Aalto,Finland","institution_ids":["https://openalex.org/I9927081"]},{"raw_affiliation_string":"Dept. of Signal Processing and Acoustics, Aalto University, Aalto, Finland","institution_ids":["https://openalex.org/I9927081"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.2106,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.89935377,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4933","last_page":"4937"},"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":0.9947999715805054,"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/T11698","display_name":"Underwater Acoustics Research","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.726193368434906},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6835201382637024},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.653997540473938},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5226927995681763},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5215513706207275},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.5175320506095886},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.5064383745193481},{"id":"https://openalex.org/keywords/complex-normal-distribution","display_name":"Complex normal distribution","score":0.5021355152130127},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.472013920545578},{"id":"https://openalex.org/keywords/multivariate-normal-distribution","display_name":"Multivariate normal distribution","score":0.45847609639167786},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.43150559067726135},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4129783809185028},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.381276935338974},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.35351407527923584},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.2724218964576721},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.11681506037712097}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.726193368434906},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6835201382637024},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.653997540473938},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5226927995681763},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5215513706207275},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.5175320506095886},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.5064383745193481},{"id":"https://openalex.org/C201080289","wikidata":"https://www.wikidata.org/wiki/Q5156587","display_name":"Complex normal distribution","level":3,"score":0.5021355152130127},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.472013920545578},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.45847609639167786},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.43150559067726135},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4129783809185028},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.381276935338974},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.35351407527923584},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.2724218964576721},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.11681506037712097},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icassp43922.2022.9746740","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9746740","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:aaltodoc.aalto.fi:123456789/115823","is_oa":true,"landing_page_url":"https://research.aalto.fi/en/publications/83e02916-074d-4ef8-acef-ad14fa631ad9","pdf_url":null,"source":{"id":"https://openalex.org/S4306401662","display_name":"Aaltodoc (Aalto University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I9927081","host_organization_name":"Aalto University","host_organization_lineage":["https://openalex.org/I9927081"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"acceptedVersion"}],"best_oa_location":{"id":"pmh:oai:aaltodoc.aalto.fi:123456789/115823","is_oa":true,"landing_page_url":"https://research.aalto.fi/en/publications/83e02916-074d-4ef8-acef-ad14fa631ad9","pdf_url":null,"source":{"id":"https://openalex.org/S4306401662","display_name":"Aaltodoc (Aalto University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I9927081","host_organization_name":"Aalto University","host_organization_lineage":["https://openalex.org/I9927081"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"acceptedVersion"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.7699999809265137,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W653761051","https://openalex.org/W1834265579","https://openalex.org/W1967504554","https://openalex.org/W1972206072","https://openalex.org/W1984614739","https://openalex.org/W1991465250","https://openalex.org/W1992511687","https://openalex.org/W1997222146","https://openalex.org/W1999177119","https://openalex.org/W2004140730","https://openalex.org/W2078601999","https://openalex.org/W2084163181","https://openalex.org/W2119933098","https://openalex.org/W2142652110","https://openalex.org/W2147665979","https://openalex.org/W2169264135","https://openalex.org/W2290170958","https://openalex.org/W2768966190","https://openalex.org/W2795431618","https://openalex.org/W2884244916","https://openalex.org/W2889696859","https://openalex.org/W2900338801","https://openalex.org/W2970041326","https://openalex.org/W3010676838","https://openalex.org/W3047717353","https://openalex.org/W3098352114","https://openalex.org/W3140950033","https://openalex.org/W3167256084","https://openalex.org/W4211079158"],"related_works":["https://openalex.org/W2102148524","https://openalex.org/W2314720829","https://openalex.org/W4385074335","https://openalex.org/W2626189183","https://openalex.org/W2174469529","https://openalex.org/W2502532714","https://openalex.org/W4310510912","https://openalex.org/W1983705072","https://openalex.org/W1571013631","https://openalex.org/W1964167411"],"abstract_inverted_index":{"Recent":[0],"investigations":[1],"indicate":[2],"that":[3,28],"Sparse":[4],"Bayesian":[5],"Learning":[6],"(SBL)":[7],"is":[8,109],"lacking":[9],"in":[10],"robustness.":[11],"We":[12,55],"derive":[13],"a":[14,33],"robust":[15,119],"and":[16,82,114,127],"sparse":[17],"Direction":[18],"of":[19,80,105],"Arrival":[20],"(DOA)":[21],"estimation":[22],"framework":[23],"based":[24],"on":[25],"the":[26,29,46,48,60,63,69,72,83,91,95,106],"assumption":[27],"array":[30],"data":[31],"has":[32],"centered":[34],"(zero-mean)":[35],"complex":[36,66,73],"elliptically":[37],"symmetric":[38,65],"(ES)":[39],"distribution":[40],"with":[41,77,130],"finite":[42],"second-order":[43],"moments.":[44],"In":[45],"derivation,":[47],"loss":[49,84],"function":[50],"can":[51],"be":[52],"quite":[53],"general.":[54],"consider":[56],"three":[57],"specific":[58],"choices:":[59],"ML-loss":[61,70],"for":[62,71,85,111,124,133],"circularly":[64],"Gaussian":[67,89,134],"distribution,":[68],"multivariate":[74],"t-distribution":[75],"(MVT)":[76],"\u03bd":[78],"degrees":[79],"freedom,":[81],"Huber\u2019s":[86],"M-estimator.":[87],"For":[88],"loss,":[90],"method":[92],"reduces":[93],"to":[94],"classic":[96],"SBL":[97,120,132],"method.":[98],"The":[99,118],"root":[100],"mean":[101],"square":[102],"DOA":[103],"performance":[104],"derived":[107],"estimators":[108,121],"discussed":[110],"Gaussian,":[112],"MVT,":[113],"\u03f5-":[115],"contaminated":[116],"noise.":[117,135],"perform":[122],"well":[123],"all":[125],"cases":[126],"nearly":[128],"identical":[129],"classical":[131]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2025-10-10T00:00:00"}