{"id":"https://openalex.org/W2120630977","doi":"https://doi.org/10.1109/tsp.2010.2084084","title":"Location Estimation of a Random Signal Source Based on Correlated Sensor Observations","display_name":"Location Estimation of a Random Signal Source Based on Correlated Sensor Observations","publication_year":2010,"publication_date":"2010-10-08","ids":{"openalex":"https://openalex.org/W2120630977","doi":"https://doi.org/10.1109/tsp.2010.2084084","mag":"2120630977"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2010.2084084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2010.2084084","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/A5053184908","display_name":"Ashok Sundaresan","orcid":null},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ashok Sundaresan","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA","[Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., Syracuse, NY, USA]"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA","institution_ids":["https://openalex.org/I70983195"]},{"raw_affiliation_string":"[Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., Syracuse, NY, USA]","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018292481","display_name":"Pramod K. Varshney","orcid":"https://orcid.org/0000-0003-4504-5088"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pramod K. Varshney","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA","[Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., Syracuse, NY, USA]"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA","institution_ids":["https://openalex.org/I70983195"]},{"raw_affiliation_string":"[Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., Syracuse, NY, USA]","institution_ids":["https://openalex.org/I70983195"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5053184908"],"corresponding_institution_ids":["https://openalex.org/I70983195"],"apc_list":null,"apc_paid":null,"fwci":7.6678,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.97240547,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"59","issue":"2","first_page":"787","last_page":"799"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9984999895095825,"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"}},"topics":[{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9984999895095825,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9983000159263611,"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/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9783999919891357,"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/copula","display_name":"Copula (linguistics)","score":0.8202873468399048},{"id":"https://openalex.org/keywords/likelihood-function","display_name":"Likelihood function","score":0.6549104452133179},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5640478134155273},{"id":"https://openalex.org/keywords/marginal-distribution","display_name":"Marginal distribution","score":0.5435253977775574},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.5427168011665344},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.5053687691688538},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4982450008392334},{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.48935383558273315},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.48812565207481384},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.4585447609424591},{"id":"https://openalex.org/keywords/parametric-model","display_name":"Parametric model","score":0.4579983055591583},{"id":"https://openalex.org/keywords/marginal-likelihood","display_name":"Marginal likelihood","score":0.4524106979370117},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.4514046907424927},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44284191727638245},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4411274790763855},{"id":"https://openalex.org/keywords/multivariate-normal-distribution","display_name":"Multivariate normal distribution","score":0.43819087743759155},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.42525625228881836},{"id":"https://openalex.org/keywords/maximum-likelihood-sequence-estimation","display_name":"Maximum likelihood sequence estimation","score":0.41866257786750793},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.41248440742492676},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.3676590919494629},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3045080304145813},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.20252561569213867}],"concepts":[{"id":"https://openalex.org/C17618745","wikidata":"https://www.wikidata.org/wiki/Q207509","display_name":"Copula (linguistics)","level":2,"score":0.8202873468399048},{"id":"https://openalex.org/C89106044","wikidata":"https://www.wikidata.org/wiki/Q45284","display_name":"Likelihood function","level":3,"score":0.6549104452133179},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5640478134155273},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.5435253977775574},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.5427168011665344},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.5053687691688538},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4982450008392334},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.48935383558273315},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.48812565207481384},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.4585447609424591},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.4579983055591583},{"id":"https://openalex.org/C95923904","wikidata":"https://www.wikidata.org/wiki/Q6760420","display_name":"Marginal likelihood","level":3,"score":0.4524106979370117},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.4514046907424927},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44284191727638245},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4411274790763855},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.43819087743759155},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.42525625228881836},{"id":"https://openalex.org/C191462741","wikidata":"https://www.wikidata.org/wiki/Q6795902","display_name":"Maximum likelihood sequence estimation","level":3,"score":0.41866257786750793},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.41248440742492676},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.3676590919494629},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3045080304145813},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.20252561569213867}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2010.2084084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2010.2084084","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":[],"funders":[{"id":"https://openalex.org/F4320309625","display_name":"Boise State University","ror":"https://ror.org/02e3zdp86"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W250076511","https://openalex.org/W614670535","https://openalex.org/W1496292299","https://openalex.org/W1525038591","https://openalex.org/W1650310283","https://openalex.org/W1751616979","https://openalex.org/W1965392255","https://openalex.org/W1968371014","https://openalex.org/W1974255563","https://openalex.org/W1981903823","https://openalex.org/W1987433487","https://openalex.org/W1994672023","https://openalex.org/W2019011597","https://openalex.org/W2060050130","https://openalex.org/W2066449383","https://openalex.org/W2114404361","https://openalex.org/W2119436275","https://openalex.org/W2120138268","https://openalex.org/W2123811769","https://openalex.org/W2124555964","https://openalex.org/W2126076156","https://openalex.org/W2128657437","https://openalex.org/W2141975880","https://openalex.org/W2142635246","https://openalex.org/W2144724708","https://openalex.org/W2148615436","https://openalex.org/W2151946422","https://openalex.org/W2158196600","https://openalex.org/W2166052353","https://openalex.org/W2167344069","https://openalex.org/W2168175751","https://openalex.org/W2171206152","https://openalex.org/W2171873103","https://openalex.org/W2329015511","https://openalex.org/W2478738321","https://openalex.org/W2770017452","https://openalex.org/W2799002609","https://openalex.org/W2801840425","https://openalex.org/W3149745985","https://openalex.org/W4241861175","https://openalex.org/W4243185644","https://openalex.org/W4246673598","https://openalex.org/W4255582690"],"related_works":["https://openalex.org/W4388397594","https://openalex.org/W2356093187","https://openalex.org/W164417758","https://openalex.org/W2186819079","https://openalex.org/W3023909498","https://openalex.org/W4233488282","https://openalex.org/W4287728430","https://openalex.org/W1763060499","https://openalex.org/W2141324124","https://openalex.org/W2027315755"],"abstract_inverted_index":{"The":[0,30,75],"problem":[1,76],"of":[2,5,8,14,49,55,66,71,77,92,106,114],"location":[3],"estimation":[4,21],"a":[6,12,43,96,115],"source":[7,112,117],"random":[9],"signals":[10],"using":[11,25],"network":[13],"sensors":[15,35],"is":[16,28,60,88,100,118],"considered.":[17],"A":[18],"novel":[19],"maximum-likelihood":[20],"(MLE)":[22],"based":[23],"approach":[24,124],"copula":[26,81],"functions":[27,70],"proposed.":[29],"measurements":[31],"received":[32],"at":[33],"the":[34,47,51,64,67,72,79,85,104,122],"are":[36],"often":[37],"spatially":[38],"correlated":[39],"and":[40,95,125],"characterized":[41],"by":[42],"multivariate":[44],"distribution.":[45],"Using":[46],"theory":[48],"copulas,":[50],"joint":[52,86],"parametric":[53],"density":[54],"sensor":[56,73],"observations":[57],"(joint":[58],"likelihood)":[59],"approximated":[61],"assuming":[62],"only":[63],"knowledge":[65],"marginal":[68],"likelihood":[69,87],"observations.":[74],"selecting":[78],"best":[80],"function":[82],"to":[83,102,120],"model":[84,93,97],"approached":[89],"as":[90],"one":[91],"selection":[94,107],"fusion":[98],"strategy":[99],"used":[101],"reduce":[103],"effect":[105],"bias.":[108],"An":[109],"example":[110],"involving":[111],"localization":[113],"Poisson":[116],"presented":[119],"illustrate":[121],"proposed":[123],"demonstrate":[126],"its":[127],"performance.":[128]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":10},{"year":2012,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
