{"id":"https://openalex.org/W2615863827","doi":"https://doi.org/10.1109/tsp.2017.2706187","title":"Information-Theoretic Compressive Sensing Kernel Optimization and Bayesian Cram\u00e9r\u2013Rao Bound for Time Delay Estimation","display_name":"Information-Theoretic Compressive Sensing Kernel Optimization and Bayesian Cram\u00e9r\u2013Rao Bound for Time Delay Estimation","publication_year":2017,"publication_date":"2017-05-18","ids":{"openalex":"https://openalex.org/W2615863827","doi":"https://doi.org/10.1109/tsp.2017.2706187","mag":"2615863827"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2017.2706187","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2017.2706187","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/A5015909551","display_name":"Yujie Gu","orcid":"https://orcid.org/0000-0003-1312-1605"},"institutions":[{"id":"https://openalex.org/I4210145244","display_name":"Radar (United States)","ror":"https://ror.org/05c5a2504","country_code":"US","type":"company","lineage":["https://openalex.org/I4210145244"]},{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yujie Gu","raw_affiliation_strings":["School of Electrical and Computer Engineering, Advanced Radar Research Center, University of Oklahoma, Norman, OK, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Advanced Radar Research Center, University of Oklahoma, Norman, OK, USA","institution_ids":["https://openalex.org/I8692664","https://openalex.org/I4210145244"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008913364","display_name":"Nathan A. Goodman","orcid":"https://orcid.org/0000-0002-7939-1395"},"institutions":[{"id":"https://openalex.org/I4210145244","display_name":"Radar (United States)","ror":"https://ror.org/05c5a2504","country_code":"US","type":"company","lineage":["https://openalex.org/I4210145244"]},{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nathan A. Goodman","raw_affiliation_strings":["School of Electrical and Computer Engineering, Advanced Radar Research Center, University of Oklahoma, Norman, OK, USA"],"raw_orcid":"https://orcid.org/0000-0002-7939-1395","affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, Advanced Radar Research Center, University of Oklahoma, Norman, OK, USA","institution_ids":["https://openalex.org/I8692664","https://openalex.org/I4210145244"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":12.1027,"has_fulltext":false,"cited_by_count":89,"citation_normalized_percentile":{"value":0.9941452,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"65","issue":"17","first_page":"4525","last_page":"4537"},"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9995999932289124,"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/compressed-sensing","display_name":"Compressed sensing","score":0.7626876831054688},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6374128460884094},{"id":"https://openalex.org/keywords/cram\u00e9r\u2013rao-bound","display_name":"Cram\u00e9r\u2013Rao bound","score":0.5825538635253906},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5764644145965576},{"id":"https://openalex.org/keywords/nyquist-rate","display_name":"Nyquist rate","score":0.5512595176696777},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5160021781921387},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.44418755173683167},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.42799851298332214},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.3118462562561035},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25936320424079895},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.2543479800224304},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.21826297044754028},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.12695267796516418},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.09026774764060974},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.08307042717933655}],"concepts":[{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7626876831054688},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6374128460884094},{"id":"https://openalex.org/C4978587","wikidata":"https://www.wikidata.org/wiki/Q1138810","display_name":"Cram\u00e9r\u2013Rao bound","level":3,"score":0.5825538635253906},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5764644145965576},{"id":"https://openalex.org/C65914096","wikidata":"https://www.wikidata.org/wiki/Q6273772","display_name":"Nyquist rate","level":4,"score":0.5512595176696777},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5160021781921387},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.44418755173683167},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.42799851298332214},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3118462562561035},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25936320424079895},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2543479800224304},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21826297044754028},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.12695267796516418},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.09026774764060974},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.08307042717933655},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2017.2706187","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2017.2706187","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/G2237409082","display_name":null,"funder_award_id":"N66001-10-1-4079","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"}],"funders":[{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W653761051","https://openalex.org/W1500801837","https://openalex.org/W1620131277","https://openalex.org/W1840522340","https://openalex.org/W2004790242","https://openalex.org/W2022265006","https://openalex.org/W2052232897","https://openalex.org/W2069667906","https://openalex.org/W2076550855","https://openalex.org/W2082050285","https://openalex.org/W2090192367","https://openalex.org/W2091256848","https://openalex.org/W2096282089","https://openalex.org/W2102701524","https://openalex.org/W2108807677","https://openalex.org/W2110768232","https://openalex.org/W2111616148","https://openalex.org/W2119227377","https://openalex.org/W2119667497","https://openalex.org/W2122548617","https://openalex.org/W2124047279","https://openalex.org/W2129638195","https://openalex.org/W2134008802","https://openalex.org/W2138446376","https://openalex.org/W2141426461","https://openalex.org/W2151590659","https://openalex.org/W2163059050","https://openalex.org/W2165709293","https://openalex.org/W2167404137","https://openalex.org/W2169500239","https://openalex.org/W2296616510","https://openalex.org/W2478708596","https://openalex.org/W2559897227","https://openalex.org/W2709121271","https://openalex.org/W3100302110","https://openalex.org/W3101339175","https://openalex.org/W4246841922","https://openalex.org/W4250955649","https://openalex.org/W4254831297"],"related_works":["https://openalex.org/W2889294138","https://openalex.org/W2150066276","https://openalex.org/W4387870235","https://openalex.org/W2046270755","https://openalex.org/W2005032074","https://openalex.org/W2356275480","https://openalex.org/W4250067459","https://openalex.org/W2520516827","https://openalex.org/W3030866127","https://openalex.org/W2938042419"],"abstract_inverted_index":{"With":[0],"the":[1,9,37,45,58,111,117,127,132,135,149,162,170,173,182,189,204,214,225],"adoption":[2],"of":[3,11,47,60,116,161,172,227],"arbitrary":[4],"and":[5,23,134,181,218,243],"increasingly":[6],"wideband":[7],"signals,":[8],"design":[10,79],"modern":[12],"radar":[13,230],"systems":[14],"continues":[15],"to":[16,32,78,92,178,221],"be":[17,63,76],"limited":[18],"by":[19,43,109,141],"analog-to-digital":[20],"converter":[21],"technology":[22],"data":[24],"throughput":[25],"bottlenecks.":[26],"Meanwhile,":[27],"compressive":[28,81],"sensing":[29,176,193],"(CS)":[30],"promises":[31],"reduce":[33],"sampling":[34],"rates":[35],"below":[36],"Nyquist":[38],"rate":[39],"for":[40,96,154,192],"some":[41],"applications":[42],"constraining":[44],"set":[46],"possible":[48],"signals.":[49],"In":[50,84,165],"many":[51],"practical":[52],"applications,":[53],"detailed":[54],"prior":[55],"knowledge":[56],"on":[57],"signals":[59],"interest":[61],"can":[62,75,196],"learned":[64],"from":[65],"training":[66],"data,":[67],"existing":[68],"track":[69],"information,":[70],"and/or":[71],"other":[72],"sources,":[73],"which":[74,200],"used":[77],"better":[80],"measurement":[82],"kernels.":[83],"this":[85],"paper,":[86],"we":[87,146,168,212,233],"use":[88,213],"an":[89],"information-theoretic":[90],"approach":[91],"optimize":[93],"CS":[94,122,163,228,235],"kernels":[95,177],"time":[97,118,136,155],"delay":[98,137,156],"estimation.":[99],"The":[100,120],"measurements":[101,133],"are":[102],"modeled":[103],"via":[104],"a":[105,112,142,159],"Gaussian":[106],"mixture":[107],"model":[108],"discretizing":[110],"priori":[113],"probability":[114],"distribution":[115],"delay.":[119],"optimal":[121,175],"kernel":[123,194],"that":[124,188],"approximately":[125],"maximizes":[126],"Shannon":[128],"mutual":[129],"information":[130],"between":[131],"is":[138,201],"then":[139],"found":[140],"gradient-based":[143],"search.":[144],"Furthermore,":[145],"also":[147],"derive":[148],"Bayesian":[150,183,205,215],"Cram\u00e9r-Rao":[151],"bound":[152],"(CRB)":[153],"estimation":[157],"as":[158],"function":[160],"kernel.":[164],"numerical":[166],"simulations,":[167],"compare":[169],"performance":[171],"proposed":[174,190],"random":[179],"projections":[180],"CRB.":[184],"Simulation":[185],"results":[186,220],"demonstrate":[187],"technique":[191],"optimization":[195],"significantly":[197],"improve":[198],"performance,":[199],"consistent":[202],"with":[203],"CRB":[206,216],"versus":[207,238],"signal-to-noise":[208],"ratio":[209],"(SNR).":[210],"Finally,":[211],"expressions":[217],"simulation":[219],"make":[222],"conclusions":[223],"about":[224],"usefulness":[226],"in":[229,241],"applications.":[231],"Specifically,":[232],"discuss":[234],"SNR":[236],"loss":[237],"resolution":[239],"improvement":[240],"SNR-":[242],"resolution-limited":[244],"scenarios.":[245]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":25},{"year":2017,"cited_by_count":8}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
