{"id":"https://openalex.org/W2339936177","doi":"https://doi.org/10.1109/tsp.2016.2531634","title":"A Newborn Track Detection and State Estimation Algorithm Using Bernoulli Random Finite Sets","display_name":"A Newborn Track Detection and State Estimation Algorithm Using Bernoulli Random Finite Sets","publication_year":2016,"publication_date":"2016-02-18","ids":{"openalex":"https://openalex.org/W2339936177","doi":"https://doi.org/10.1109/tsp.2016.2531634","mag":"2339936177"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2016.2531634","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2016.2531634","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/A5004326966","display_name":"Baehoon Choi","orcid":"https://orcid.org/0009-0006-0487-7661"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Baehoon Choi","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050292399","display_name":"Seongkeun Park","orcid":"https://orcid.org/0000-0002-4868-9404"},"institutions":[{"id":"https://openalex.org/I49946491","display_name":"Hyundai Motors (South Korea)","ror":"https://ror.org/016kvft77","country_code":"KR","type":"company","lineage":["https://openalex.org/I197312522","https://openalex.org/I49946491"]},{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seongkeun Park","raw_affiliation_strings":["Advanced Driver Assistance System Recognition Development Team, Hyundai Motors, Gyeonggi, Korea","School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Advanced Driver Assistance System Recognition Development Team, Hyundai Motors, Gyeonggi, Korea","institution_ids":["https://openalex.org/I49946491"]},{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065415014","display_name":"Euntai Kim","orcid":"https://orcid.org/0000-0002-0975-8390"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Euntai Kim","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5004326966"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":2.1424,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.90239138,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"64","issue":"10","first_page":"2660","last_page":"2674"},"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":0.9994999766349792,"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":0.9994999766349792,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9745000004768372,"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":0.9710000157356262,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bernoullis-principle","display_name":"Bernoulli's principle","score":0.6793991327285767},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5643005967140198},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5550878643989563},{"id":"https://openalex.org/keywords/track-before-detect","display_name":"Track-before-detect","score":0.47582700848579407},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4636683464050293},{"id":"https://openalex.org/keywords/finite-state","display_name":"Finite state","score":0.4288051128387451},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3282259702682495},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.3194499611854553},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2790158987045288},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.12496113777160645},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1227448582649231},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08827206492424011}],"concepts":[{"id":"https://openalex.org/C152361515","wikidata":"https://www.wikidata.org/wiki/Q181328","display_name":"Bernoulli's principle","level":2,"score":0.6793991327285767},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5643005967140198},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5550878643989563},{"id":"https://openalex.org/C173028925","wikidata":"https://www.wikidata.org/wiki/Q7831461","display_name":"Track-before-detect","level":4,"score":0.47582700848579407},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4636683464050293},{"id":"https://openalex.org/C2983497884","wikidata":"https://www.wikidata.org/wiki/Q176452","display_name":"Finite state","level":3,"score":0.4288051128387451},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3282259702682495},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.3194499611854553},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2790158987045288},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.12496113777160645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1227448582649231},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08827206492424011},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2016.2531634","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2016.2531634","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":[{"id":"https://openalex.org/G7880215560","display_name":null,"funder_award_id":"NRF-2013R1A2A2A01015624","funder_id":"https://openalex.org/F4320322349","funder_display_name":"Ministry of Education, Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320322349","display_name":"Ministry of Education, Science and Technology","ror":"https://ror.org/01p262204"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W34992941","https://openalex.org/W1557595030","https://openalex.org/W1965106715","https://openalex.org/W1983681129","https://openalex.org/W1985621395","https://openalex.org/W1999616028","https://openalex.org/W2014787937","https://openalex.org/W2023286597","https://openalex.org/W2023516725","https://openalex.org/W2032325588","https://openalex.org/W2048605796","https://openalex.org/W2050882265","https://openalex.org/W2082393168","https://openalex.org/W2098973553","https://openalex.org/W2103124057","https://openalex.org/W2105905583","https://openalex.org/W2106873007","https://openalex.org/W2110354007","https://openalex.org/W2120846837","https://openalex.org/W2126885789","https://openalex.org/W2127923214","https://openalex.org/W2137585588","https://openalex.org/W2142300547","https://openalex.org/W2161435744","https://openalex.org/W4301885439","https://openalex.org/W6633511732","https://openalex.org/W6675414472"],"related_works":["https://openalex.org/W1974854782","https://openalex.org/W2071899530","https://openalex.org/W3037703633","https://openalex.org/W2013097455","https://openalex.org/W303980170","https://openalex.org/W2351491280","https://openalex.org/W3206405635","https://openalex.org/W2386767533","https://openalex.org/W2078803338","https://openalex.org/W2371447506"],"abstract_inverted_index":{"In":[0,48],"multi-target":[1],"tracking":[2],"(MTT)":[3],"problems,":[4],"there":[5],"are":[6],"many":[7],"important":[8,36],"issues":[9],"that":[10],"affect":[11],"performance,":[12],"including":[13],"statistical":[14,74],"filtering,":[15],"measurement-target":[16],"association,":[17],"and":[18,28,56,87,117],"estimating":[19],"the":[20,63,98,101],"number":[21],"of":[22,65,80,100],"targets.":[23],"While":[24],"newborn":[25,53,82],"target":[26,83],"detection":[27,55],"state":[29,57],"estimation":[30,58],"should":[31],"also":[32],"be":[33],"considered":[34],"as":[35],"factors":[37],"in":[38],"MTT,":[39],"only":[40],"a":[41,51,81,88,109],"few":[42],"studies":[43],"have":[44],"addressed":[45],"these":[46],"topics.":[47],"this":[49],"paper,":[50],"novel":[52],"track":[54],"method":[59,103],"is":[60,84,92,104],"proposed":[61,93,102],"using":[62,94],"concept":[64],"Bernoulli":[66],"random":[67],"finite":[68,72],"sets.":[69],"The":[70],"posterior":[71],"set":[73],"probability":[75,112],"density":[76,114],"function":[77],"(FISST":[78],"PDF)":[79],"analytically":[85],"derived,":[86],"tractable":[89],"implementation":[90],"scheme":[91],"importance":[95],"sampling.":[96],"Finally,":[97],"validity":[99],"demonstrated":[105],"via":[106],"integration":[107],"with":[108],"Gaussian":[110],"mixture":[111],"hypothesis":[113],"(GM-PHD)":[115],"filter":[116],"subsequent":[118],"application":[119],"to":[120],"MTT":[121],"problems.":[122]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"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"}