{"id":"https://openalex.org/W2148128172","doi":"https://doi.org/10.1109/tsp.2014.2311962","title":"Beyond MAP Estimation With the Track-Oriented Multiple Hypothesis Tracker","display_name":"Beyond MAP Estimation With the Track-Oriented Multiple Hypothesis Tracker","publication_year":2014,"publication_date":"2014-03-13","ids":{"openalex":"https://openalex.org/W2148128172","doi":"https://doi.org/10.1109/tsp.2014.2311962","mag":"2148128172"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2014.2311962","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2014.2311962","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/A5006459153","display_name":"Andrew J. Frank","orcid":null},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Andrew Frank","raw_affiliation_strings":["Department of Computer Science, University of California, Irvine, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of California, Irvine, CA, USA","institution_ids":["https://openalex.org/I204250578"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077460655","display_name":"Padhraic Smyth","orcid":"https://orcid.org/0000-0001-9971-8378"},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Padhraic Smyth","raw_affiliation_strings":["Department of Computer Science, University of California, Irvine, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of California, Irvine, CA, USA","institution_ids":["https://openalex.org/I204250578"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048047705","display_name":"Alexander Ihler","orcid":"https://orcid.org/0000-0002-4331-1015"},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander Ihler","raw_affiliation_strings":["Department of Computer Science, University of California, Irvine, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of California, Irvine, CA, USA","institution_ids":["https://openalex.org/I204250578"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5006459153"],"corresponding_institution_ids":["https://openalex.org/I204250578"],"apc_list":null,"apc_paid":null,"fwci":1.227,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.85363451,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"62","issue":"9","first_page":"2413","last_page":"2423"},"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":0.9962999820709229,"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":0.9962999820709229,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9702000021934509,"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":0.9577999711036682,"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/robustness","display_name":"Robustness (evolution)","score":0.6430025100708008},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.6366176605224609},{"id":"https://openalex.org/keywords/data-association","display_name":"Data association","score":0.6340190172195435},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6337215304374695},{"id":"https://openalex.org/keywords/factor-graph","display_name":"Factor graph","score":0.6253141164779663},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5615174770355225},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5064042806625366},{"id":"https://openalex.org/keywords/message-passing","display_name":"Message passing","score":0.48935383558273315},{"id":"https://openalex.org/keywords/marginal-likelihood","display_name":"Marginal likelihood","score":0.47912120819091797},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4787437319755554},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.4784688651561737},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.44455134868621826},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4340379238128662},{"id":"https://openalex.org/keywords/landmark","display_name":"Landmark","score":0.410768985748291},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.39535757899284363},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.2584742307662964},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.21542856097221375},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19233930110931396}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6430025100708008},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.6366176605224609},{"id":"https://openalex.org/C2983325608","wikidata":"https://www.wikidata.org/wiki/Q17084606","display_name":"Data association","level":3,"score":0.6340190172195435},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6337215304374695},{"id":"https://openalex.org/C159246509","wikidata":"https://www.wikidata.org/wiki/Q5428725","display_name":"Factor graph","level":3,"score":0.6253141164779663},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5615174770355225},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5064042806625366},{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.48935383558273315},{"id":"https://openalex.org/C95923904","wikidata":"https://www.wikidata.org/wiki/Q6760420","display_name":"Marginal likelihood","level":3,"score":0.47912120819091797},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4787437319755554},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.4784688651561737},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44455134868621826},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4340379238128662},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.410768985748291},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39535757899284363},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.2584742307662964},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.21542856097221375},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19233930110931396},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2014.2311962","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2014.2311962","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":[{"score":0.6800000071525574,"id":"https://metadata.un.org/sdg/1","display_name":"No poverty"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1490670270","https://openalex.org/W1529072446","https://openalex.org/W1568122762","https://openalex.org/W1577099551","https://openalex.org/W1665207485","https://openalex.org/W1876515821","https://openalex.org/W1963718895","https://openalex.org/W1999425090","https://openalex.org/W2014898029","https://openalex.org/W2055111541","https://openalex.org/W2073680033","https://openalex.org/W2101295974","https://openalex.org/W2118388220","https://openalex.org/W2121812207","https://openalex.org/W2127923214","https://openalex.org/W2137688837","https://openalex.org/W2137813581","https://openalex.org/W2148877605","https://openalex.org/W2155680787","https://openalex.org/W2163364417","https://openalex.org/W2169415915","https://openalex.org/W2184346291","https://openalex.org/W2906926620","https://openalex.org/W2963123876","https://openalex.org/W2963720626","https://openalex.org/W6631658127","https://openalex.org/W6634658447","https://openalex.org/W6639316738","https://openalex.org/W6650396104","https://openalex.org/W6677632369","https://openalex.org/W6681488117","https://openalex.org/W6682192342","https://openalex.org/W6683951949","https://openalex.org/W6758076146"],"related_works":["https://openalex.org/W1783992599","https://openalex.org/W2114899076","https://openalex.org/W2045588782","https://openalex.org/W2124697778","https://openalex.org/W2135468550","https://openalex.org/W2133422797","https://openalex.org/W2142673160","https://openalex.org/W2181917637","https://openalex.org/W4284711868","https://openalex.org/W2145296384"],"abstract_inverted_index":{"The":[0],"track-oriented":[1],"multiple":[2,12],"hypothesis":[3],"tracker":[4,197],"(TOMHT)":[5],"is":[6,22,97],"a":[7,15,25,28,111,174],"popular":[8],"algorithm":[9,181],"for":[10],"tracking":[11,19],"targets":[13],"in":[14,170,183],"cluttered":[16],"environment.":[17],"In":[18,129,186],"parlance":[20],"it":[21,33,45,105],"known":[23],"as":[24,154],"multi-scan,":[26],"maximum":[27],"posteriori":[29],"(MAP)":[30],"estimator-multi-scan":[31],"because":[32,44],"enumerates":[34],"possible":[35,158],"data":[36,50,83,118],"associations":[37],"jointly":[38],"over":[39,77,149],"several":[40],"scans,":[41],"and":[42,121],"MAP":[43,72],"seeks":[46],"the":[47,54,59,78,116,150,155,171,178],"most":[48],"likely":[49],"association":[51,84,119],"conditioned":[52],"on":[53,62],"observations.":[55],"This":[56],"paper":[57],"extends":[58],"TOMHT,":[60],"building":[61],"its":[63],"internal":[64],"representation":[65,114],"to":[66,125,143,199],"support":[67],"probabilistic":[68],"queries":[69],"other":[70],"than":[71],"estimation.":[73],"Specifically,":[74],"by":[75],"summing":[76],"TOMHT's":[79,117],"pruned":[80],"space":[81],"of":[82,91,115,157,177],"hypotheses":[85,159],"one":[86],"can":[87],"compute":[88],"marginal":[89,136],"probabilities":[90],"individual":[92],"tracks.":[93],"Since":[94],"this":[95],"summation":[96,148],"generally":[98],"intractable,":[99],"any":[100],"practical":[101],"implementation":[102],"must":[103],"replace":[104],"with":[106],"an":[107,130],"approximation.":[108],"We":[109,161],"introduce":[110],"factor":[112],"graph":[113],"posterior":[120],"use":[122],"variational":[123],"message-passing":[124,140],"approximate":[126,193],"track":[127,165],"marginals.":[128],"empirical":[131],"evaluation,":[132],"we":[133],"show":[134,163],"that":[135,164],"estimates":[137],"computed":[138,145],"through":[139,146],"compare":[141],"favorably":[142],"those":[144],"explicit":[147],"k-best":[151],"hypotheses,":[152],"especially":[153],"number":[156],"increases.":[160],"also":[162],"marginals":[166,194],"enable":[167],"parameter":[168,202],"estimation":[169],"TOMHT":[172],"via":[173],"natural":[175],"extension":[176],"expectation":[179],"maximization":[180],"used":[182],"single-target":[184],"tracking.":[185],"our":[187],"experiments,":[188],"online":[189],"EM":[190],"updates":[191],"using":[192],"significantly":[195],"increased":[196],"robustness":[198],"poor":[200],"initial":[201],"specification.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
