{"id":"https://openalex.org/W3169314463","doi":"https://doi.org/10.1109/ieeeconf51394.2020.9443335","title":"METRIC-Bayes: Measurements Estimation for Tracking in High Clutter using Bayesian Nonparametrics","display_name":"METRIC-Bayes: Measurements Estimation for Tracking in High Clutter using Bayesian Nonparametrics","publication_year":2020,"publication_date":"2020-11-01","ids":{"openalex":"https://openalex.org/W3169314463","doi":"https://doi.org/10.1109/ieeeconf51394.2020.9443335","mag":"3169314463"},"language":"en","primary_location":{"id":"doi:10.1109/ieeeconf51394.2020.9443335","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf51394.2020.9443335","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","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/A5010743010","display_name":"Bahman Moraffah","orcid":null},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Bahman Moraffah","raw_affiliation_strings":["Arizona State University,Department of Electrical, Computer, and Energy,Tempe,AZ","Department of Electrical, Computer, and Energy, Arizona State University, Tempe, AZ"],"affiliations":[{"raw_affiliation_string":"Arizona State University,Department of Electrical, Computer, and Energy,Tempe,AZ","institution_ids":["https://openalex.org/I55732556"]},{"raw_affiliation_string":"Department of Electrical, Computer, and Energy, Arizona State University, Tempe, AZ","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017517054","display_name":"Christ D. Richmond","orcid":"https://orcid.org/0000-0001-6392-0267"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christ Richmond","raw_affiliation_strings":["Arizona State University,Department of Electrical, Computer, and Energy,Tempe,AZ","Department of Electrical, Computer, and Energy, Arizona State University, Tempe, AZ"],"affiliations":[{"raw_affiliation_string":"Arizona State University,Department of Electrical, Computer, and Energy,Tempe,AZ","institution_ids":["https://openalex.org/I55732556"]},{"raw_affiliation_string":"Department of Electrical, Computer, and Energy, Arizona State University, Tempe, AZ","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047078657","display_name":"Raha Moraffah","orcid":"https://orcid.org/0000-0002-6891-2925"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Raha Moraffah","raw_affiliation_strings":["School of Computing, Informatics, and Decision Systems Engineering, Arizona State University,Tempe,AZ","School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ"],"affiliations":[{"raw_affiliation_string":"School of Computing, Informatics, and Decision Systems Engineering, Arizona State University,Tempe,AZ","institution_ids":["https://openalex.org/I55732556"]},{"raw_affiliation_string":"School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069246416","display_name":"Antonia Papandreou\u2010Suppappola","orcid":"https://orcid.org/0000-0002-5223-6256"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Antonia Papandreou-Suppappola","raw_affiliation_strings":["Arizona State University,Department of Electrical, Computer, and Energy,Tempe,AZ","Department of Electrical, Computer, and Energy, Arizona State University, Tempe, AZ"],"affiliations":[{"raw_affiliation_string":"Arizona State University,Department of Electrical, Computer, and Energy,Tempe,AZ","institution_ids":["https://openalex.org/I55732556"]},{"raw_affiliation_string":"Department of Electrical, Computer, and Energy, Arizona State University, Tempe, AZ","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5010743010"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":0.1326,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.58933154,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1518","last_page":"1522"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9990000128746033,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9990000128746033,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9659000039100647,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9534000158309937,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.9035732746124268},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6523945331573486},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6232317686080933},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5396023392677307},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49223342537879944},{"id":"https://openalex.org/keywords/dirichlet-process","display_name":"Dirichlet process","score":0.474344402551651},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.45488667488098145},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.4482559859752655},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.42083945870399475},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.4161333441734314},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4105774164199829},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2604947090148926},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.20104175806045532},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11246562004089355}],"concepts":[{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.9035732746124268},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6523945331573486},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6232317686080933},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5396023392677307},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49223342537879944},{"id":"https://openalex.org/C2781280628","wikidata":"https://www.wikidata.org/wiki/Q5280766","display_name":"Dirichlet process","level":3,"score":0.474344402551651},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.45488667488098145},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4482559859752655},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.42083945870399475},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.4161333441734314},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4105774164199829},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2604947090148926},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.20104175806045532},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11246562004089355},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ieeeconf51394.2020.9443335","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf51394.2020.9443335","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 54th Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1551893515","https://openalex.org/W2069429561","https://openalex.org/W2098296991","https://openalex.org/W2099514122","https://openalex.org/W2109957730","https://openalex.org/W2118603200","https://openalex.org/W2141766286","https://openalex.org/W2151967501","https://openalex.org/W2152486142","https://openalex.org/W2153797006","https://openalex.org/W2183060819","https://openalex.org/W2545563415","https://openalex.org/W2920067846","https://openalex.org/W2938113204","https://openalex.org/W2963117122","https://openalex.org/W2973137244","https://openalex.org/W3010955404","https://openalex.org/W3012133589","https://openalex.org/W3017884031","https://openalex.org/W3195275699","https://openalex.org/W4234801319","https://openalex.org/W4287813005","https://openalex.org/W4308951891","https://openalex.org/W6682569104","https://openalex.org/W6767956649","https://openalex.org/W6774773839","https://openalex.org/W6775036208","https://openalex.org/W6776227368","https://openalex.org/W6800058211"],"related_works":["https://openalex.org/W2130674020","https://openalex.org/W2093748878","https://openalex.org/W2333771223","https://openalex.org/W2120056845","https://openalex.org/W1981531423","https://openalex.org/W4394861761","https://openalex.org/W1977371217","https://openalex.org/W2035264131","https://openalex.org/W1679012645","https://openalex.org/W2042770250"],"abstract_inverted_index":{"Robust":[0],"tracking":[1,57,176],"of":[2,32,38,72,82,99,110,123,134,153,180],"a":[3,6,46,58,69,121,145,161],"target":[4,60,102,135,155,168],"in":[5,61,103,160],"clutter":[7,87,105,137,189],"environment":[8,63,106],"is":[9,157],"an":[10,62],"important":[11],"and":[12,22,94,136,178,206],"challenging":[13],"task.":[14],"In":[15,41,117,191],"recent":[16],"years,":[17],"the":[18,30,97,100,130,141,154,166,175],"nearest":[19,204],"neighbor":[20,205],"methods":[21,34,201],"probabilistic":[23],"data":[24,208],"association":[25,209],"filters":[26],"were":[27],"proposed.":[28],"However,":[29],"performance":[31,177],"these":[33],"diminishes":[35],"as":[36,203],"number":[37,71,109],"measurements":[39,55,73,138,156],"increases.":[40],"this":[42],"paper,":[43],"we":[44,119,193],"propose":[45],"robust":[47],"generative":[48],"approach":[49],"to":[50,128,164],"effectively":[51],"model":[52],"multiple":[53],"sensor":[54,76],"for":[56],"moving":[59,101],"with":[64,78,88,107],"high":[65,104,188],"clutter.":[66],"We":[67,92,170],"assume":[68],"time-dependent":[70],"that":[74,140,174,195],"include":[75],"observations":[77],"unknown":[79,108],"origin,":[80],"some":[81],"which":[83],"may":[84],"only":[85],"contain":[86],"no":[89],"additional":[90],"information.":[91],"robustly":[93],"accurately":[95],"estimate":[96,165],"trajectory":[98],"clutters":[111],"by":[112,186],"employing":[113],"Bayesian":[114,125,162],"nonparametric":[115,126],"modeling.":[116],"particular,":[118],"employ":[120],"class":[122],"joint":[124,131],"models":[127],"construct":[129],"prior":[132,152],"distribution":[133],"such":[139,202],"conditional":[142],"distributions":[143],"follow":[144],"Dirichlet":[146,150],"process.":[147],"The":[148],"marginalized":[149],"process":[151],"then":[158],"used":[159],"tracker":[163],"dynamically-varying":[167],"state.":[169],"show":[171,194],"through":[172],"experiments":[173],"effectiveness":[179],"our":[181,196],"proposed":[182,197],"framework":[183],"are":[184],"increased":[185],"suppressing":[187],"measurements.":[190],"addition,":[192],"method":[198],"outperforms":[199],"existing":[200],"probability":[207],"filters.":[210]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
