{"id":"https://openalex.org/W2405801843","doi":"https://doi.org/10.1109/icassp.2016.7472204","title":"Distributionally robust chance-constrained minimum variance beamforming","display_name":"Distributionally robust chance-constrained minimum variance beamforming","publication_year":2016,"publication_date":"2016-03-01","ids":{"openalex":"https://openalex.org/W2405801843","doi":"https://doi.org/10.1109/icassp.2016.7472204","mag":"2405801843"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2016.7472204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472204","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5100750868","display_name":"Xiao Zhang","orcid":"https://orcid.org/0000-0003-4886-7327"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiao Zhang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102977435","display_name":"Qiang Feng","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Feng","raw_affiliation_strings":["Tsinghua Space Center, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua Space Center, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053547427","display_name":"Ning Ge","orcid":"https://orcid.org/0000-0002-4922-7025"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Ge","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053012932","display_name":"Jianhua L\u00fc","orcid":"https://orcid.org/0000-0002-0314-9172"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianhua Lu","raw_affiliation_strings":["Tsinghua Space Center, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua Space Center, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100750868"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":1.0087,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.75955698,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2881","last_page":"2885"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9995999932289124,"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":0.9995999932289124,"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/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9987000226974487,"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/covariance","display_name":"Covariance","score":0.6048364043235779},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.601262092590332},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5304471850395203},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5067344307899475},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5048587918281555},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.50440514087677},{"id":"https://openalex.org/keywords/robust-optimization","display_name":"Robust optimization","score":0.4729183316230774},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.44938579201698303},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.44171440601348877},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.43229204416275024},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.42510873079299927},{"id":"https://openalex.org/keywords/bounded-function","display_name":"Bounded function","score":0.4188891351222992},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.40383777022361755},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2722013592720032},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20765936374664307}],"concepts":[{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6048364043235779},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.601262092590332},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5304471850395203},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5067344307899475},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5048587918281555},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.50440514087677},{"id":"https://openalex.org/C193254401","wikidata":"https://www.wikidata.org/wiki/Q2160088","display_name":"Robust optimization","level":2,"score":0.4729183316230774},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44938579201698303},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.44171440601348877},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.43229204416275024},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.42510873079299927},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.4188891351222992},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40383777022361755},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2722013592720032},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20765936374664307},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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},{"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/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","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":[{"id":"doi:10.1109/icassp.2016.7472204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472204","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":17,"referenced_works":["https://openalex.org/W568673721","https://openalex.org/W653761051","https://openalex.org/W1572308468","https://openalex.org/W1967729795","https://openalex.org/W2005602327","https://openalex.org/W2021472931","https://openalex.org/W2057077708","https://openalex.org/W2061293389","https://openalex.org/W2066218102","https://openalex.org/W2103049885","https://openalex.org/W2113168978","https://openalex.org/W2124733114","https://openalex.org/W2145486800","https://openalex.org/W2153473676","https://openalex.org/W2166117654","https://openalex.org/W2798344481","https://openalex.org/W4245577611"],"related_works":["https://openalex.org/W2788344745","https://openalex.org/W2062336688","https://openalex.org/W2910677864","https://openalex.org/W3195104199","https://openalex.org/W2046078371","https://openalex.org/W2383820648","https://openalex.org/W1503294153","https://openalex.org/W2886934452","https://openalex.org/W1489099099","https://openalex.org/W2024369332"],"abstract_inverted_index":{"This":[0],"paper":[1],"studies":[2],"distributionally":[3,39],"robust":[4,40,78,123,139],"chance-constrained":[5],"minimum":[6],"variance":[7],"beamforming.":[8],"In":[9],"contrast":[10],"to":[11,37,111],"deterministic":[12,130],"modeling":[13],"of":[14,87,137],"the":[15,21,28,32,38,48,51,63,67,77,85,101,105,129,135],"steering":[16],"vector,":[17],"our":[18],"approach":[19],"models":[20],"uncertainty":[22,49],"statistically":[23],"via":[24,141],"distributions.":[25],"We":[26],"select":[27],"weights":[29,79],"that":[30,43,120],"minimize":[31],"combined":[33],"output":[34],"power":[35],"subject":[36],"chance":[41],"constraint":[42],"for":[44,80,128],"all":[45],"distributions":[46,90],"in":[47],"set,":[50],"gain":[52],"should":[53],"exceed":[54],"unity":[55],"with":[56,62,91],"high":[57],"probability.":[58],"Our":[59],"discussion":[60],"begins":[61],"simplest":[64],"case":[65],"where":[66,100],"distributional":[68,82,98],"set":[69,86],"contains":[70],"only":[71],"Gaussian":[72],"distribution;":[73],"then":[74],"we":[75,133],"derive":[76],"three":[81],"sets,":[83],"namely,":[84],"(central":[88],"symmetric)":[89],"known":[92,113],"mean":[93,102],"and":[94,96,109],"covariance,":[95],"a":[97],"model":[99],"is":[103],"known,":[104],"components":[106],"are":[107],"independent":[108],"belong":[110],"some":[112],"bounded":[114],"intervals.":[115],"It":[116],"can":[117],"be":[118],"seen":[119],"these":[121,138],"four":[122],"beamformers":[124,140],"provide":[125],"statistical":[126],"interpretation":[127],"counterpart.":[131],"Finally,":[132],"demonstrate":[134],"performance":[136],"several":[142],"numerical":[143],"examples.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}