{"id":"https://openalex.org/W2119344157","doi":"https://doi.org/10.1109/tit.1980.1056247","title":"A two-dimensional maximum entropy spectral estimator","display_name":"A two-dimensional maximum entropy spectral estimator","publication_year":1980,"publication_date":"1980-09-01","ids":{"openalex":"https://openalex.org/W2119344157","doi":"https://doi.org/10.1109/tit.1980.1056247","mag":"2119344157"},"language":"en","primary_location":{"id":"doi:10.1109/tit.1980.1056247","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.1980.1056247","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"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 Information Theory","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/A5006694769","display_name":"S. Roucos","orcid":null},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"S. Roucos","raw_affiliation_strings":["Department of Electrical Engineering, University of Florida, Gainesville, FL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Florida, Gainesville, FL, USA","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045979967","display_name":"D. G. Childers","orcid":null},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"D. Childers","raw_affiliation_strings":["Department of Electrical Engineering, University of Florida, Gainesville, FL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Florida, Gainesville, FL, USA","institution_ids":["https://openalex.org/I33213144"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5006694769"],"corresponding_institution_ids":["https://openalex.org/I33213144"],"apc_list":null,"apc_paid":null,"fwci":4.4352,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.93900415,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":"26","issue":"5","first_page":"554","last_page":"560"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/maximum-entropy-spectral-estimation","display_name":"Maximum entropy spectral estimation","score":0.8414822220802307},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.7248713970184326},{"id":"https://openalex.org/keywords/autocorrelation","display_name":"Autocorrelation","score":0.699947714805603},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6711020469665527},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.6172323822975159},{"id":"https://openalex.org/keywords/extrapolation","display_name":"Extrapolation","score":0.5336694717407227},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5106425881385803},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4938221871852875},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4843962788581848},{"id":"https://openalex.org/keywords/covariance-function","display_name":"Covariance function","score":0.4719468951225281},{"id":"https://openalex.org/keywords/spectral-density-estimation","display_name":"Spectral density estimation","score":0.4719098210334778},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4324624538421631},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.41225147247314453},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4119187593460083},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.37254399061203003},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.16682803630828857},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08675670623779297}],"concepts":[{"id":"https://openalex.org/C89735579","wikidata":"https://www.wikidata.org/wiki/Q6795894","display_name":"Maximum entropy spectral estimation","level":3,"score":0.8414822220802307},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7248713970184326},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.699947714805603},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6711020469665527},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.6172323822975159},{"id":"https://openalex.org/C132459708","wikidata":"https://www.wikidata.org/wiki/Q744069","display_name":"Extrapolation","level":2,"score":0.5336694717407227},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5106425881385803},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4938221871852875},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4843962788581848},{"id":"https://openalex.org/C137250428","wikidata":"https://www.wikidata.org/wiki/Q5178897","display_name":"Covariance function","level":3,"score":0.4719468951225281},{"id":"https://openalex.org/C30049272","wikidata":"https://www.wikidata.org/wiki/Q6555326","display_name":"Spectral density estimation","level":3,"score":0.4719098210334778},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4324624538421631},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.41225147247314453},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4119187593460083},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.37254399061203003},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.16682803630828857},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08675670623779297},{"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/tit.1980.1056247","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.1980.1056247","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"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 Information Theory","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1485321578","https://openalex.org/W1496923911","https://openalex.org/W1654590594","https://openalex.org/W1966264494","https://openalex.org/W1966654446","https://openalex.org/W1976555829","https://openalex.org/W1982890547","https://openalex.org/W2018847547","https://openalex.org/W2022554507","https://openalex.org/W2040544615","https://openalex.org/W2066218102","https://openalex.org/W2068710327","https://openalex.org/W2078403926","https://openalex.org/W2090560000","https://openalex.org/W2132930716","https://openalex.org/W2169395484","https://openalex.org/W2320196987","https://openalex.org/W2332237006","https://openalex.org/W2782523680","https://openalex.org/W3093664000","https://openalex.org/W4214498742","https://openalex.org/W6629474112","https://openalex.org/W6784656030"],"related_works":["https://openalex.org/W2164375742","https://openalex.org/W2132953787","https://openalex.org/W262848098","https://openalex.org/W2625846045","https://openalex.org/W4321516847","https://openalex.org/W1966654446","https://openalex.org/W2073470747","https://openalex.org/W2292590677","https://openalex.org/W238822977","https://openalex.org/W2901143001"],"abstract_inverted_index":{"Using":[0,92],"ideas":[1],"from":[2],"one-dimensional":[3],"maximum":[4,42],"entropy":[5,28,43],"spectral":[6,10,90,103],"estimation":[7],"a":[8,30],"two-dimensional":[9,17,71,89,111],"estimator":[11],"is":[12,45,68,87,113,119],"derived":[13],"by":[14,76],"extrapolating":[15],"the":[16,27,50,57,60,65,77,83,88,102,110],"sampled":[18],"autocorrelation":[19,85],"(or":[20,37],"covariance)":[21],"function.":[22],"The":[23,35,70,79,121],"method":[24,94],"used":[25],"maximizes":[26],"of":[29,32,59,82],"set":[31],"random":[33,52],"variables.":[34],"extrapolation":[36,72],"prediction)":[38],"process":[39],"under":[40],"this":[41,93],"condition":[44],"shown":[46],"to":[47,49,56,100],"correspond":[48],"most":[51],"extension":[53],"or":[54],"equivalently":[55],"maximization":[58],"mean-square":[61],"prediction":[62],"error":[63],"when":[64],"optimum":[66],"predictor":[67],"used.":[69],"must":[73],"he":[74],"terminated":[75],"investigator.":[78],"Fourier":[80],"transform":[81],"extrapolated":[84],"function":[86],"estimator.":[91],"one":[95],"can":[96],"apply":[97],"windowing":[98],"prior":[99],"calculating":[101],"estimate.":[104],"A":[105],"specific":[106],"algorithm":[107,122],"for":[108],"estimating":[109],"spectrum":[112],"presented,":[114],"and":[115,126],"its":[116],"computational":[117],"complexity":[118],"estimated.":[120],"has":[123],"been":[124],"programmed":[125],"computer":[127],"examples":[128],"are":[129],"presented.":[130]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}