{"id":"https://openalex.org/W4386363019","doi":"https://doi.org/10.1109/tsp.2023.3309459","title":"Multichannel Frequency Estimation in Challenging Scenarios via Structured Matrix Embedding and Recovery (StruMER)","display_name":"Multichannel Frequency Estimation in Challenging Scenarios via Structured Matrix Embedding and Recovery (StruMER)","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4386363019","doi":"https://doi.org/10.1109/tsp.2023.3309459"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2023.3309459","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2023.3309459","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/A5101446831","display_name":"Xunmeng Wu","orcid":"https://orcid.org/0000-0003-3742-925X"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xunmeng Wu","raw_affiliation_strings":["School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China","Peng Cheng Laboratory, Shenzhen, Guangdong, China","Pazhou Laboratory (Huangpu), Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0003-3742-925X","affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, Guangdong, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"Pazhou Laboratory (Huangpu), Guangzhou, Guangdong, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082539585","display_name":"Zai Yang","orcid":"https://orcid.org/0000-0002-9502-5176"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zai Yang","raw_affiliation_strings":["School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China","Peng Cheng Laboratory, Shenzhen, Guangdong, China","Pazhou Laboratory (Huangpu), Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0002-9502-5176","affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, Guangdong, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"Pazhou Laboratory (Huangpu), Guangzhou, Guangdong, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109280540","display_name":"Zongben Xu","orcid":"https://orcid.org/0000-0002-4066-2338"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zongben Xu","raw_affiliation_strings":["School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China","Peng Cheng Laboratory, Shenzhen, Guangdong, China","Pazhou Laboratory (Huangpu), Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0002-4066-2338","affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, Guangdong, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"Pazhou Laboratory (Huangpu), Guangzhou, Guangdong, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.0021,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.86792917,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"71","issue":null,"first_page":"3242","last_page":"3256"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9993000030517578,"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/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9993000030517578,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/toeplitz-matrix","display_name":"Toeplitz matrix","score":0.6965677738189697},{"id":"https://openalex.org/keywords/hankel-matrix","display_name":"Hankel matrix","score":0.5955175757408142},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5871647596359253},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5695940852165222},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5331705212593079},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.5232499837875366},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.5185433030128479},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.5083422064781189},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49258190393447876},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4889184534549713},{"id":"https://openalex.org/keywords/block-matrix","display_name":"Block matrix","score":0.47771191596984863},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.45220234990119934},{"id":"https://openalex.org/keywords/low-rank-approximation","display_name":"Low-rank approximation","score":0.45139026641845703},{"id":"https://openalex.org/keywords/matrix-completion","display_name":"Matrix completion","score":0.4185151755809784},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.39671164751052856},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.12512487173080444},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1103697419166565}],"concepts":[{"id":"https://openalex.org/C147710293","wikidata":"https://www.wikidata.org/wiki/Q849428","display_name":"Toeplitz matrix","level":2,"score":0.6965677738189697},{"id":"https://openalex.org/C25023664","wikidata":"https://www.wikidata.org/wiki/Q1575637","display_name":"Hankel matrix","level":2,"score":0.5955175757408142},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5871647596359253},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5695940852165222},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5331705212593079},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.5232499837875366},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.5185433030128479},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.5083422064781189},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49258190393447876},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4889184534549713},{"id":"https://openalex.org/C85817219","wikidata":"https://www.wikidata.org/wiki/Q884772","display_name":"Block matrix","level":3,"score":0.47771191596984863},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.45220234990119934},{"id":"https://openalex.org/C90199385","wikidata":"https://www.wikidata.org/wiki/Q6692777","display_name":"Low-rank approximation","level":3,"score":0.45139026641845703},{"id":"https://openalex.org/C2778459887","wikidata":"https://www.wikidata.org/wiki/Q6787865","display_name":"Matrix completion","level":3,"score":0.4185151755809784},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39671164751052856},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.12512487173080444},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1103697419166565},{"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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2023.3309459","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2023.3309459","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/G3045771181","display_name":null,"funder_award_id":"PCL2023AS1-2","funder_id":"https://openalex.org/F4320318558","funder_display_name":"Peng Cheng Laboratory"},{"id":"https://openalex.org/G3266017007","display_name":null,"funder_award_id":"61977053","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G330146058","display_name":null,"funder_award_id":"12371464","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320318558","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W210359992","https://openalex.org/W1541064563","https://openalex.org/W1814500939","https://openalex.org/W1846534543","https://openalex.org/W1946620893","https://openalex.org/W1952629836","https://openalex.org/W1968157422","https://openalex.org/W1999177119","https://openalex.org/W2000804746","https://openalex.org/W2002377668","https://openalex.org/W2003207663","https://openalex.org/W2018744547","https://openalex.org/W2018962290","https://openalex.org/W2025688234","https://openalex.org/W2029725686","https://openalex.org/W2034942620","https://openalex.org/W2043746281","https://openalex.org/W2055849441","https://openalex.org/W2058077717","https://openalex.org/W2059618367","https://openalex.org/W2068242608","https://openalex.org/W2095695857","https://openalex.org/W2096217494","https://openalex.org/W2101658087","https://openalex.org/W2103519107","https://openalex.org/W2112883107","https://openalex.org/W2113638573","https://openalex.org/W2115891560","https://openalex.org/W2117756735","https://openalex.org/W2128131274","https://openalex.org/W2133285942","https://openalex.org/W2139961842","https://openalex.org/W2142652110","https://openalex.org/W2149755721","https://openalex.org/W2153605253","https://openalex.org/W2154191559","https://openalex.org/W2156893865","https://openalex.org/W2161666352","https://openalex.org/W2162654459","https://openalex.org/W2163232332","https://openalex.org/W2166462772","https://openalex.org/W2469797310","https://openalex.org/W2528695050","https://openalex.org/W2535161902","https://openalex.org/W2548019060","https://openalex.org/W2565293665","https://openalex.org/W2604403210","https://openalex.org/W2768966190","https://openalex.org/W2781773153","https://openalex.org/W2801753252","https://openalex.org/W2956863782","https://openalex.org/W2962727817","https://openalex.org/W2962853966","https://openalex.org/W2964106337","https://openalex.org/W3101457173","https://openalex.org/W3105393233","https://openalex.org/W3183444633","https://openalex.org/W4205293427","https://openalex.org/W4229706427","https://openalex.org/W4291910335","https://openalex.org/W4292363360","https://openalex.org/W4386065303","https://openalex.org/W4392302641","https://openalex.org/W6847574128"],"related_works":["https://openalex.org/W4386863313","https://openalex.org/W4387676286","https://openalex.org/W4388584692","https://openalex.org/W2964184729","https://openalex.org/W4300656036","https://openalex.org/W2611130496","https://openalex.org/W4206230498","https://openalex.org/W2986925424","https://openalex.org/W2921727095","https://openalex.org/W2801753252"],"abstract_inverted_index":{"Multichannel":[0],"frequency":[1,30],"estimation":[2,31],"with":[3,163],"incomplete":[4],"data":[5,42],"and":[6,18,44,76,79,129,157],"miscellaneous":[7],"noises":[8],"arises":[9],"in":[10,32,108,116,145],"array":[11],"signal":[12,63],"processing,":[13],"modal":[14],"analysis,":[15],"wireless":[16],"communications,":[17],"so":[19],"on.":[20],"In":[21],"this":[22],"paper,":[23],"we":[24],"consider":[25],"maximum-likelihood(-like)":[26],"optimization":[27,56,84],"methods":[28],"for":[29],"which":[33,109],"proper":[34],"objective":[35],"functions":[36],"are":[37,114,135],"adopted":[38],"subject":[39],"to":[40,53,101,137],"observed":[41],"patterns":[43],"noise":[45],"types.":[46],"We":[47],"propose":[48],"a":[49,67],"universal":[50],"signal-domain":[51],"approach":[52,122],"solve":[54,102],"the":[55,60,81,103],"problems":[57,85,107],"by":[58],"embedding":[59,128],"noiseless":[61],"multichannel":[62],"of":[64,69,74,96,112],"interest":[65],"into":[66],"series":[68],"low-rank":[70],"positive-semidefinite":[71],"block":[72],"matrices":[73],"Hankel":[75],"Toeplitz":[77],"submatrices":[78],"formulating":[80],"original":[82],"parameter-domain":[83],"as":[86,125,161],"equivalent":[87],"structured":[88,126],"matrix":[89,105,127],"recovery":[90,106,130],"problems.":[91],"The":[92,120],"alternating":[93],"direction":[94],"method":[95],"multipliers":[97],"(ADMM)":[98],"is":[99,123],"applied":[100],"resulting":[104],"both":[110],"subproblems":[111],"ADMM":[113],"solved":[115],"(nearly)":[117],"closed":[118],"form.":[119],"proposed":[121],"termed":[124],"(StruMER).":[131],"Extensive":[132],"numerical":[133],"simulations":[134],"provided":[136],"demonstrate":[138],"that":[139],"StruMER":[140],"has":[141],"improved":[142],"threshold":[143],"performances":[144],"various":[146],"challenging":[147],"scenarios,":[148],"e.g.,":[149],"limited":[150],"data,":[151],"low":[152],"signal-to-noise":[153],"ratio,":[154],"impulsive":[155],"noise,":[156],"closely":[158],"spaced":[159],"frequencies,":[160],"compared":[162],"state-of-the-art":[164],"methods.":[165]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":6}],"updated_date":"2026-07-25T09:21:30.201066","created_date":"2025-10-10T00:00:00"}