{"id":"https://openalex.org/W1999665925","doi":"https://doi.org/10.1109/globalsip.2014.7032203","title":"Frequency estimator performance analysis with compressive sensing or non-uniform sampling","display_name":"Frequency estimator performance analysis with compressive sensing or non-uniform sampling","publication_year":2014,"publication_date":"2014-12-01","ids":{"openalex":"https://openalex.org/W1999665925","doi":"https://doi.org/10.1109/globalsip.2014.7032203","mag":"1999665925"},"language":"en","primary_location":{"id":"doi:10.1109/globalsip.2014.7032203","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2014.7032203","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","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/A5111378671","display_name":"Peter Wyckoff","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Peter S. Wyckoff","raw_affiliation_strings":["PreDetection Solutions Scottsdale, Arizona, USA","PreDetection Solutions, Scottsdale, Arizona, USA"],"affiliations":[{"raw_affiliation_string":"PreDetection Solutions Scottsdale, Arizona, USA","institution_ids":[]},{"raw_affiliation_string":"PreDetection Solutions, Scottsdale, Arizona, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5111378671"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3154,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.58619068,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"29","issue":null,"first_page":"674","last_page":"678"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9994999766349792,"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/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9987999796867371,"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/estimator","display_name":"Estimator","score":0.7544621229171753},{"id":"https://openalex.org/keywords/cram\u00e9r\u2013rao-bound","display_name":"Cram\u00e9r\u2013Rao bound","score":0.6962639689445496},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.6752951145172119},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.6556923985481262},{"id":"https://openalex.org/keywords/alias","display_name":"Alias","score":0.5963302850723267},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5344326496124268},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5175884962081909},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.498607873916626},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.465692400932312},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.45530739426612854},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4389723837375641},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4294099807739258},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4273773431777954},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.41182103753089905},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.21140414476394653},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.09060096740722656},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08492147922515869},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.07917758822441101}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7544621229171753},{"id":"https://openalex.org/C4978587","wikidata":"https://www.wikidata.org/wiki/Q1138810","display_name":"Cram\u00e9r\u2013Rao bound","level":3,"score":0.6962639689445496},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.6752951145172119},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.6556923985481262},{"id":"https://openalex.org/C46681722","wikidata":"https://www.wikidata.org/wiki/Q4725589","display_name":"Alias","level":2,"score":0.5963302850723267},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5344326496124268},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5175884962081909},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.498607873916626},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.465692400932312},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.45530739426612854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4389723837375641},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4294099807739258},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4273773431777954},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.41182103753089905},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.21140414476394653},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.09060096740722656},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08492147922515869},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.07917758822441101},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","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/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/globalsip.2014.7032203","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2014.7032203","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W633002199","https://openalex.org/W1500801837","https://openalex.org/W1538014708","https://openalex.org/W1591116419","https://openalex.org/W1740216830","https://openalex.org/W1983695700","https://openalex.org/W2001188690","https://openalex.org/W2023438354","https://openalex.org/W2029749781","https://openalex.org/W2104266187","https://openalex.org/W2127316913","https://openalex.org/W2155166937","https://openalex.org/W2161101713","https://openalex.org/W4206180546","https://openalex.org/W4235713725","https://openalex.org/W6656562206","https://openalex.org/W6929274189"],"related_works":["https://openalex.org/W4385605198","https://openalex.org/W2151266859","https://openalex.org/W2056017980","https://openalex.org/W2333004434","https://openalex.org/W4256550813","https://openalex.org/W4400966522","https://openalex.org/W1557487237","https://openalex.org/W4248617250","https://openalex.org/W275149381","https://openalex.org/W2997042634"],"abstract_inverted_index":{"Uniform":[0],"sampling":[1,12,90,132],"may":[2,96],"ensure":[3],"alias-free":[4,105],"signal":[5,58],"representation.":[6],"Using":[7],"complex-valued":[8],"samples,":[9],"the":[10,16,20,26,46,66,71,80,103,115,123],"uniform":[11],"rate":[13],"must":[14],"exceed":[15],"analog":[17,22,106],"bandwidth.":[18],"Consequently,":[19],"system":[21],"bandwidth":[23,107],"requirement":[24],"constrains":[25,39],"time":[27],"spanned":[28],"by":[29,45,60],"N":[30],"uniformly":[31],"spaced":[32],"samples.":[33,112],"Ultimately,":[34],"acquiring":[35],"this":[36],"limited":[37],"timespan":[38,100],"frequency":[40,68,137],"estimator":[41],"variance":[42],"as":[43],"evidenced":[44],"Cramer-Rao":[47,124],"lower":[48,125],"bound.":[49,126],"Frequency":[50],"estimators":[51,64],"commonly":[52],"use":[53],"maximum-likelihood":[54],"estimation":[55],"for":[56],"a":[57,75,98,128],"corrupted":[59],"Gaussian":[61],"noise.":[62],"These":[63],"compute":[65],"peak":[67],"correlation":[69],"between":[70],"received":[72],"samples":[73],"and":[74,108,121],"known":[76],"waveform":[77],"template.":[78],"Since":[79],"hypothesized":[81],"basis":[82],"functions":[83],"are":[84],"known,":[85],"compressive":[86],"sensing":[87],"or":[88],"non-uniform":[89],"provides":[91],"an":[92],"alternative.":[93],"Either":[94],"alternative":[95,131],"observe":[97],"greater":[99],"while":[101],"preserving":[102],"same":[104],"without":[109],"recording":[110],"more":[111],"This":[113],"increases":[114],"average":[116],"Fisher":[117],"information":[118],"per":[119],"sample":[120],"decreases":[122],"As":[127],"result,":[129],"these":[130],"techniques":[133],"can":[134],"deliver":[135],"improved":[136],"estimates.":[138]},"counts_by_year":[{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
