{"id":"https://openalex.org/W2296630171","doi":"https://doi.org/10.1109/tsp.2015.2512527","title":"Average SINR Calculation of a Persymmetric Sample Matrix Inversion Beamformer","display_name":"Average SINR Calculation of a Persymmetric Sample Matrix Inversion Beamformer","publication_year":2015,"publication_date":"2015-12-25","ids":{"openalex":"https://openalex.org/W2296630171","doi":"https://doi.org/10.1109/tsp.2015.2512527","mag":"2296630171"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2015.2512527","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2015.2512527","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/A5100361920","display_name":"Jun Liu","orcid":"https://orcid.org/0000-0002-7193-0622"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jun Liu","raw_affiliation_strings":["Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059682113","display_name":"Weijian Liu","orcid":"https://orcid.org/0000-0002-0330-8073"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weijian Liu","raw_affiliation_strings":["Wuhan Radar Academy, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Wuhan Radar Academy, Wuhan, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100411968","display_name":"Hongwei Liu","orcid":"https://orcid.org/0000-0003-4046-163X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongwei Liu","raw_affiliation_strings":["Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011047781","display_name":"Bo Chen","orcid":"https://orcid.org/0000-0003-2702-3758"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Chen","raw_affiliation_strings":["Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065367615","display_name":"Xiang\u2010Gen Xia","orcid":"https://orcid.org/0000-0002-5599-7683"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]},{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Xiang-Gen Xia","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA","Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA","institution_ids":["https://openalex.org/I86501945"]},{"raw_affiliation_string":"Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036145098","display_name":"Fengzhou Dai","orcid":"https://orcid.org/0000-0003-2166-2516"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fengzhou Dai","raw_affiliation_strings":["Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100361920"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":14.5942,"has_fulltext":false,"cited_by_count":71,"citation_normalized_percentile":{"value":0.98724983,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"64","issue":"8","first_page":"2135","last_page":"2145"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10891","display_name":"Radar Systems and Signal Processing","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10891","display_name":"Radar Systems and Signal Processing","score":0.9998000264167786,"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"}},{"id":"https://openalex.org/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9993000030517578,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9970999956130981,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sample-matrix-inversion","display_name":"Sample matrix inversion","score":0.6555156111717224},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.6260524988174438},{"id":"https://openalex.org/keywords/wishart-distribution","display_name":"Wishart distribution","score":0.5723477005958557},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5145975351333618},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5057470798492432},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5026986598968506},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4541683495044708},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.44910547137260437},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.4190899431705475},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.33571040630340576},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.09210419654846191}],"concepts":[{"id":"https://openalex.org/C62403268","wikidata":"https://www.wikidata.org/wiki/Q7410141","display_name":"Sample matrix inversion","level":3,"score":0.6555156111717224},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.6260524988174438},{"id":"https://openalex.org/C33962027","wikidata":"https://www.wikidata.org/wiki/Q1930697","display_name":"Wishart distribution","level":3,"score":0.5723477005958557},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5145975351333618},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5057470798492432},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5026986598968506},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4541683495044708},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.44910547137260437},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.4190899431705475},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.33571040630340576},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.09210419654846191}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2015.2512527","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2015.2512527","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":[{"id":"https://openalex.org/G5474006814","display_name":null,"funder_award_id":"61571349","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6612962111","display_name":null,"funder_award_id":"61501351","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7228381803","display_name":null,"funder_award_id":"61501505","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7708652996","display_name":null,"funder_award_id":"NCET-13-0945","funder_id":"https://openalex.org/F4320334924","funder_display_name":"Program for New Century Excellent Talents in University"},{"id":"https://openalex.org/G935788038","display_name":null,"funder_award_id":"61372132","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334924","display_name":"Program for New Century Excellent Talents in University","ror":"https://ror.org/01mv9t934"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W653761051","https://openalex.org/W1964366482","https://openalex.org/W1966105707","https://openalex.org/W2005602327","https://openalex.org/W2013438974","https://openalex.org/W2027923082","https://openalex.org/W2039882402","https://openalex.org/W2057077708","https://openalex.org/W2061873652","https://openalex.org/W2063677402","https://openalex.org/W2066218102","https://openalex.org/W2071572195","https://openalex.org/W2075261934","https://openalex.org/W2077602393","https://openalex.org/W2087527144","https://openalex.org/W2100975169","https://openalex.org/W2106094087","https://openalex.org/W2106698944","https://openalex.org/W2109316322","https://openalex.org/W2113249205","https://openalex.org/W2121148325","https://openalex.org/W2122420047","https://openalex.org/W2123776775","https://openalex.org/W2126528280","https://openalex.org/W2126568557","https://openalex.org/W2129934596","https://openalex.org/W2143694437","https://openalex.org/W2144158572","https://openalex.org/W2145376813","https://openalex.org/W2149816072","https://openalex.org/W2152776741","https://openalex.org/W2154051216","https://openalex.org/W2156166401","https://openalex.org/W2166855863","https://openalex.org/W3179903767","https://openalex.org/W4213099427","https://openalex.org/W4293005804","https://openalex.org/W4362230038","https://openalex.org/W6682961916"],"related_works":["https://openalex.org/W3134381438","https://openalex.org/W2057690963","https://openalex.org/W2118598748","https://openalex.org/W2011956837","https://openalex.org/W2108933226","https://openalex.org/W2886934452","https://openalex.org/W1489099099","https://openalex.org/W2057077708","https://openalex.org/W2024369332","https://openalex.org/W2161781654"],"abstract_inverted_index":{"Based":[0],"on":[1,30],"the":[2,12,34,58,67,70,79,84,87,97,102,111,135,138,145,152,156,166,173,180],"Bartlett's":[3],"decomposition":[4],"of":[5,18,69,78,101,137,147,155,175],"a":[6,19,27,42,47,107],"Wishart":[7],"random":[8],"matrix,":[9],"we":[10,61,91],"study":[11],"normalized":[13,71],"output":[14,72],"signal-to-interference-plus-noise":[15],"ratio":[16],"(SINR)":[17],"sample":[20],"matrix":[21],"inversion":[22],"(SMI)":[23],"beamformer":[24,105,159,169],"with":[25,46],"exploiting":[26],"priori":[28],"information":[29],"persymmetric":[31,38,80,103,139,157,167],"structures":[32],"in":[33,86,106,172],"received":[35],"signal.":[36],"The":[37],"structure":[39,140],"exists":[40],"when":[41],"system":[43],"is":[44,141],"equipped":[45],"symmetrically":[48,53],"spaced":[49,54],"linear":[50],"array":[51],"or":[52],"pulse":[55],"trains.":[56],"In":[57,164],"matched":[59],"case,":[60],"obtain":[62],"an":[63,93],"exact":[64],"expression":[65,95],"for":[66,96],"expectation":[68],"SINR":[73,76,99,153],"(i.e.,":[74],"average":[75,98],"loss)":[77],"SMI":[81,104,158,168],"beamformer.":[82],"Considering":[83],"mismatch":[85],"signal":[88],"steering":[89],"vector,":[90],"derive":[92],"approximate":[94],"loss":[100,154],"non-homogeneous":[108],"environment":[109],"where":[110,179],"test":[112],"and":[113,150],"training":[114,148,177],"data":[115,178],"have":[116],"different":[117],"covariance":[118],"matrices.":[119],"These":[120],"theoretical":[121],"results":[122,132],"are":[123],"all":[124],"verified":[125],"by":[126],"using":[127],"Monte":[128],"Carlo":[129],"techniques.":[130],"Simulation":[131],"reveal":[133],"that":[134],"exploitation":[136],"equivalent":[142],"to":[143],"doubling":[144],"amount":[146],"data,":[149],"thus":[151],"can":[160,170],"be":[161],"significantly":[162],"reduced.":[163],"particular,":[165],"work":[171],"case":[174],"limited":[176],"traditional":[181],"beamformers":[182],"cannot":[183],"work.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":9},{"year":2016,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}