{"id":"https://openalex.org/W2042509034","doi":"https://doi.org/10.1109/tsp.2013.2271481","title":"On the Performance Bound of Sparse Estimation With Sensing Matrix Perturbation","display_name":"On the Performance Bound of Sparse Estimation With Sensing Matrix Perturbation","publication_year":2013,"publication_date":"2013-06-27","ids":{"openalex":"https://openalex.org/W2042509034","doi":"https://doi.org/10.1109/tsp.2013.2271481","mag":"2042509034"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2013.2271481","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2013.2271481","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":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1211.6401","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087492658","display_name":"Yujie Tang","orcid":"https://orcid.org/0000-0002-4921-8372"},"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":"Yujie Tang","raw_affiliation_strings":["Dept. Electronic Engineering, Tsinghua University, Beijing, China","[Dept. Electron. Eng., Tsinghua Univ., Beijing, China]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"[Dept. Electron. Eng., Tsinghua Univ., Beijing, China]","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021810355","display_name":"Laming Chen","orcid":"https://orcid.org/0009-0008-6384-3810"},"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":"Laming Chen","raw_affiliation_strings":["Dept. Electronic Engineering, Tsinghua University, Beijing, China","[Dept. Electron. Eng., Tsinghua Univ., Beijing, China]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"[Dept. Electron. Eng., Tsinghua Univ., Beijing, China]","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100621681","display_name":"Yuantao Gu","orcid":"https://orcid.org/0000-0002-8427-1021"},"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":"Yuantao Gu","raw_affiliation_strings":["Dept. Electronic Engineering, Tsinghua University, Beijing, China","[Dept. Electron. Eng., Tsinghua Univ., Beijing, China]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"[Dept. Electron. Eng., Tsinghua Univ., Beijing, China]","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":2.7984,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.89401453,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"61","issue":"17","first_page":"4372","last_page":"4386"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.7456335425376892},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6376889944076538},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5987522006034851},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.5479571223258972},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5302975177764893},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5086458921432495},{"id":"https://openalex.org/keywords/cram\u00e9r\u2013rao-bound","display_name":"Cram\u00e9r\u2013Rao bound","score":0.4952012598514557},{"id":"https://openalex.org/keywords/restricted-isometry-property","display_name":"Restricted isometry property","score":0.4902210831642151},{"id":"https://openalex.org/keywords/closed-form-expression","display_name":"Closed-form expression","score":0.46640482544898987},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.4579121470451355},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4517506957054138},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.45020949840545654},{"id":"https://openalex.org/keywords/moore\u2013penrose-pseudoinverse","display_name":"Moore\u2013Penrose pseudoinverse","score":0.4254066050052643},{"id":"https://openalex.org/keywords/expression","display_name":"Expression (computer science)","score":0.42127326130867004},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.4183603823184967},{"id":"https://openalex.org/keywords/matrix-norm","display_name":"Matrix norm","score":0.4115833342075348},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.3840353488922119},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.29538315534591675},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.19212648272514343},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.18496209383010864},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.17882072925567627},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13888168334960938},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.10577484965324402},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09672176837921143}],"concepts":[{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.7456335425376892},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6376889944076538},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5987522006034851},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.5479571223258972},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5302975177764893},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5086458921432495},{"id":"https://openalex.org/C4978587","wikidata":"https://www.wikidata.org/wiki/Q1138810","display_name":"Cram\u00e9r\u2013Rao bound","level":3,"score":0.4952012598514557},{"id":"https://openalex.org/C17902559","wikidata":"https://www.wikidata.org/wiki/Q17099734","display_name":"Restricted isometry property","level":3,"score":0.4902210831642151},{"id":"https://openalex.org/C4530962","wikidata":"https://www.wikidata.org/wiki/Q777407","display_name":"Closed-form expression","level":2,"score":0.46640482544898987},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.4579121470451355},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4517506957054138},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.45020949840545654},{"id":"https://openalex.org/C21556879","wikidata":"https://www.wikidata.org/wiki/Q43219517","display_name":"Moore\u2013Penrose pseudoinverse","level":3,"score":0.4254066050052643},{"id":"https://openalex.org/C90559484","wikidata":"https://www.wikidata.org/wiki/Q778379","display_name":"Expression (computer science)","level":2,"score":0.42127326130867004},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.4183603823184967},{"id":"https://openalex.org/C92207270","wikidata":"https://www.wikidata.org/wiki/Q939253","display_name":"Matrix norm","level":3,"score":0.4115833342075348},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.3840353488922119},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.29538315534591675},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.19212648272514343},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.18496209383010864},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.17882072925567627},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13888168334960938},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.10577484965324402},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09672176837921143},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/tsp.2013.2271481","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2013.2271481","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"},{"id":"pmh:oai:arXiv.org:1211.6401","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1211.6401","pdf_url":"https://arxiv.org/pdf/1211.6401","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.720.383","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.720.383","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://gu.ee.tsinghua.edu.cn/index.php/component/attachments/download/17/","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.759.4548","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.759.4548","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1211.6401.pdf","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1211.6401","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1211.6401","pdf_url":"https://arxiv.org/pdf/1211.6401","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W250076511","https://openalex.org/W1500801837","https://openalex.org/W1525535255","https://openalex.org/W1806269746","https://openalex.org/W1965392255","https://openalex.org/W1974086818","https://openalex.org/W1996258525","https://openalex.org/W2001466605","https://openalex.org/W2004526834","https://openalex.org/W2015418199","https://openalex.org/W2018429487","https://openalex.org/W2018637760","https://openalex.org/W2034260606","https://openalex.org/W2039479881","https://openalex.org/W2095978736","https://openalex.org/W2097323375","https://openalex.org/W2098704159","https://openalex.org/W2099100030","https://openalex.org/W2104783034","https://openalex.org/W2109515693","https://openalex.org/W2114748345","https://openalex.org/W2119667497","https://openalex.org/W2122759946","https://openalex.org/W2123457453","https://openalex.org/W2127271355","https://openalex.org/W2128203095","https://openalex.org/W2129131372","https://openalex.org/W2140856955","https://openalex.org/W2141426461","https://openalex.org/W2145096794","https://openalex.org/W2147656689","https://openalex.org/W2152044198","https://openalex.org/W2154332973","https://openalex.org/W2156377127","https://openalex.org/W2160536217","https://openalex.org/W2160979406","https://openalex.org/W2162876243","https://openalex.org/W2164452299","https://openalex.org/W2256950762","https://openalex.org/W2289917018","https://openalex.org/W2296616510","https://openalex.org/W2963322354","https://openalex.org/W2963331401","https://openalex.org/W2964253263","https://openalex.org/W3101339175","https://openalex.org/W3105340263","https://openalex.org/W4250955649","https://openalex.org/W4255776800","https://openalex.org/W4301459447","https://openalex.org/W4302561155","https://openalex.org/W6638389366"],"related_works":["https://openalex.org/W4289712135","https://openalex.org/W2950633070","https://openalex.org/W4289376593","https://openalex.org/W2969889343","https://openalex.org/W2900534014","https://openalex.org/W4301481049","https://openalex.org/W2897477974","https://openalex.org/W2670789243","https://openalex.org/W2927801616","https://openalex.org/W2953962863"],"abstract_inverted_index":{"This":[0],"paper":[1],"focuses":[2],"on":[3],"the":[4,8,12,13,17,43,49,59,69,76,88,103,106,109,119,129,132,147,159,166,169,177,185],"sparse":[5,30],"estimation":[6,31],"in":[7,36,134,146,188],"situation":[9,60],"where":[10],"both":[11],"sensing":[14,62,141,149],"matrix":[15,63,150],"and":[16,34,48,90,105,171],"measurement":[18,73],"vector":[19],"are":[20,53,181],"corrupted":[21],"by":[22],"additive":[23],"Gaussian":[24],"noises.":[25],"The":[26],"performance":[27,160],"bound":[28,46,51],"of":[29,40,108,158],"is":[32,56,65,81,95,152,163,174],"analyzed":[33],"discussed":[35],"depth.":[37],"Two":[38],"types":[39],"lower":[41],"bounds,":[42],"constrained":[44],"Cram\u00e9r-Rao":[45],"(CCRB)":[47],"Hammersley-Chapman-Robbins":[50],"(HCRB),":[52],"discussed.":[54],"It":[55,83,94,162],"shown":[57,164],"that":[58,98,165],"with":[61,71],"perturbation":[64],"more":[66],"complex":[67],"than":[68],"one":[70],"only":[72],"noise.":[74],"For":[75,125],"CCRB,":[77],"its":[78],"closed-form":[79,144],"expression":[80,138,145],"deduced.":[82],"demonstrates":[84],"a":[85,99,126,136,143,155],"gap":[86,100,167],"between":[87,102,168],"maximal":[89,170],"nonmaximal":[91,172],"support":[92],"cases.":[93],"also":[96],"revealed":[97],"lies":[101],"CCRB":[104],"MSE":[107],"oracle":[110],"pseudoinverse":[111],"estimator,":[112],"but":[113],"it":[114],"approaches":[115],"zero":[116],"asymptotically":[117],"when":[118],"problem":[120],"dimensions":[121],"tend":[122],"to":[123,183],"infinity.":[124],"tighter":[127],"bound,":[128],"HCRB,":[130],"despite":[131],"difficulty":[133],"obtaining":[135],"simple":[137],"for":[139,154,176],"general":[140],"matrix,":[142],"unit":[148],"case":[151],"derived":[153],"qualitative":[156],"study":[157],"bound.":[161],"cases":[173],"eliminated":[175],"HCRB.":[178],"Numerical":[179],"simulations":[180],"performed":[182],"verify":[184],"theoretical":[186],"results":[187],"this":[189],"paper.":[190]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":3}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}
