{"id":"https://openalex.org/W2519050609","doi":"https://doi.org/10.1109/sam.2016.7569745","title":"Wideband Sparse Bayesian Learning for DOA estimation from multiple snapshots","display_name":"Wideband Sparse Bayesian Learning for DOA estimation from multiple snapshots","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2519050609","doi":"https://doi.org/10.1109/sam.2016.7569745","mag":"2519050609"},"language":"en","primary_location":{"id":"doi:10.1109/sam.2016.7569745","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sam.2016.7569745","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","raw_type":"proceedings-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/A5004718675","display_name":"Peter Gerstoft","orcid":"https://orcid.org/0000-0002-0471-062X"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Peter Gerstoft","raw_affiliation_strings":["Marine Physics Lab, University of California San Diego, La Jolla, CA, USA"],"affiliations":[{"raw_affiliation_string":"Marine Physics Lab, University of California San Diego, La Jolla, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014100872","display_name":"Christoph F. Mecklenbr\u00e4uker","orcid":"https://orcid.org/0000-0001-9571-0379"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Christoph F. Mecklenbrauker","raw_affiliation_strings":["TU, Wireless technologies for sustainable mobility Inst. of Telecommunications, Wien, Austria"],"affiliations":[{"raw_affiliation_string":"TU, Wireless technologies for sustainable mobility Inst. of Telecommunications, Wien, Austria","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5004718675"],"corresponding_institution_ids":["https://openalex.org/I36258959"],"apc_list":null,"apc_paid":null,"fwci":1.2609,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.81046802,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9990000128746033,"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":0.9990000128746033,"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/hyperparameter","display_name":"Hyperparameter","score":0.9340233206748962},{"id":"https://openalex.org/keywords/direction-of-arrival","display_name":"Direction of arrival","score":0.6724356412887573},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6293464303016663},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6037915349006653},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.59238201379776},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5177816152572632},{"id":"https://openalex.org/keywords/wideband","display_name":"Wideband","score":0.49884796142578125},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4947652220726013},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45479917526245117},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.45339396595954895},{"id":"https://openalex.org/keywords/snapshot","display_name":"Snapshot (computer storage)","score":0.4501665234565735},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.44207802414894104},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.4183063209056854},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4038841426372528},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09463649988174438},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.0857221782207489},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08077624440193176}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.9340233206748962},{"id":"https://openalex.org/C172051844","wikidata":"https://www.wikidata.org/wiki/Q5280438","display_name":"Direction of arrival","level":3,"score":0.6724356412887573},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6293464303016663},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6037915349006653},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.59238201379776},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5177816152572632},{"id":"https://openalex.org/C2780202535","wikidata":"https://www.wikidata.org/wiki/Q4524457","display_name":"Wideband","level":2,"score":0.49884796142578125},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4947652220726013},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45479917526245117},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.45339396595954895},{"id":"https://openalex.org/C55282118","wikidata":"https://www.wikidata.org/wiki/Q252683","display_name":"Snapshot (computer storage)","level":2,"score":0.4501665234565735},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.44207802414894104},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4183063209056854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4038841426372528},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09463649988174438},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.0857221782207489},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08077624440193176},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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":[{"id":"doi:10.1109/sam.2016.7569745","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sam.2016.7569745","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W653761051","https://openalex.org/W1124870649","https://openalex.org/W1834265579","https://openalex.org/W1961498716","https://openalex.org/W1974718273","https://openalex.org/W2006035071","https://openalex.org/W2025950086","https://openalex.org/W2042565723","https://openalex.org/W2049633694","https://openalex.org/W2061725062","https://openalex.org/W2063215935","https://openalex.org/W2090989552","https://openalex.org/W2103519107","https://openalex.org/W2132626244","https://openalex.org/W2135046866","https://openalex.org/W2146172302","https://openalex.org/W2148154358","https://openalex.org/W2152279006","https://openalex.org/W2308181013","https://openalex.org/W2963906379","https://openalex.org/W3104217244","https://openalex.org/W4241068368","https://openalex.org/W4252791956","https://openalex.org/W4285719527","https://openalex.org/W6684135164"],"related_works":["https://openalex.org/W2140186469","https://openalex.org/W4390421286","https://openalex.org/W4280563792","https://openalex.org/W4389724018","https://openalex.org/W4318719684","https://openalex.org/W2359299668","https://openalex.org/W1604267224","https://openalex.org/W3088635414","https://openalex.org/W4362680572","https://openalex.org/W2116723448"],"abstract_inverted_index":{"The":[0,21,83],"directions":[1],"of":[2,5,67],"arrival":[3],"(DOA)":[4],"plane":[6],"waves":[7],"are":[8,71],"estimated":[9],"from":[10],"multi-frequency":[11],"multi-snapshot":[12],"sensor":[13],"array":[14],"data":[15],"using":[16],"Sparse":[17],"Bayesian":[18],"Learning":[19],"(SBL).":[20],"prior":[22],"for":[23,86],"the":[24,39,43,56,69,76],"source":[25,44],"amplitudes":[26],"is":[27,61,89],"assumed":[28],"to":[29],"be":[30],"independently":[31],"zero-mean":[32],"complex":[33,48],"Gaussian":[34,49,58],"distributed":[35],"with":[36,51],"hyperparameters":[37,70],"being":[38],"unknown":[40,52],"variances":[41],"(i.e.":[42],"powers).":[45],"For":[46,63],"a":[47,64],"likelihood":[50],"noise":[53],"variance":[54],"hyperparameter,":[55],"corresponding":[57],"posterior":[59],"distribution":[60],"derived.":[62],"given":[65],"number":[66],"DOAs,":[68],"automatically":[72],"selected":[73],"by":[74],"maximizing":[75],"evidence":[77],"and":[78,91],"promote":[79],"sparse":[80],"DOA":[81,87],"estimates.":[82],"SBL":[84],"scheme":[85],"estimation":[88],"discussed":[90],"evaluated":[92],"competitively":[93],"against":[94],"MUSIC.":[95]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}