{"id":"https://openalex.org/W3095206042","doi":"https://doi.org/10.1109/mfi49285.2020.9235215","title":"Bayesian Deghosting Algorithm for Multiple Target Tracking","display_name":"Bayesian Deghosting Algorithm for Multiple Target Tracking","publication_year":2020,"publication_date":"2020-09-14","ids":{"openalex":"https://openalex.org/W3095206042","doi":"https://doi.org/10.1109/mfi49285.2020.9235215","mag":"3095206042"},"language":"en","primary_location":{"id":"doi:10.1109/mfi49285.2020.9235215","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mfi49285.2020.9235215","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","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/A5018373392","display_name":"Pavel Kulmon","orcid":"https://orcid.org/0000-0003-3876-3460"},"institutions":[{"id":"https://openalex.org/I4210087455","display_name":"ERA (Czechia)","ror":"https://ror.org/007s0q763","country_code":"CZ","type":"company","lineage":["https://openalex.org/I4210087455"]},{"id":"https://openalex.org/I44504214","display_name":"Czech Technical University in Prague","ror":"https://ror.org/03kqpb082","country_code":"CZ","type":"education","lineage":["https://openalex.org/I44504214"]}],"countries":["CZ"],"is_corresponding":true,"raw_author_name":"Pavel Kulmon","raw_affiliation_strings":["Czech Technical University in Prague, Czech Republic","ERA a.s., Pardubice, Czech Republic"],"affiliations":[{"raw_affiliation_string":"Czech Technical University in Prague, Czech Republic","institution_ids":["https://openalex.org/I44504214"]},{"raw_affiliation_string":"ERA a.s., Pardubice, Czech Republic","institution_ids":["https://openalex.org/I4210087455"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5018373392"],"corresponding_institution_ids":["https://openalex.org/I4210087455","https://openalex.org/I44504214"],"apc_list":null,"apc_paid":null,"fwci":0.4116,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.70448959,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"367","last_page":"372"},"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":0.9993000030517578,"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":0.9993000030517578,"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/T10891","display_name":"Radar Systems and Signal Processing","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11698","display_name":"Underwater Acoustics Research","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7737760543823242},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.7550227642059326},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6776612997055054},{"id":"https://openalex.org/keywords/radar-tracker","display_name":"Radar tracker","score":0.6084007024765015},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5558741092681885},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.550872802734375},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5076371431350708},{"id":"https://openalex.org/keywords/data-association","display_name":"Data association","score":0.49270036816596985},{"id":"https://openalex.org/keywords/bistatic-radar","display_name":"Bistatic radar","score":0.4726375937461853},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.38529086112976074},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30130505561828613},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.13436007499694824},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.11600345373153687},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09654712677001953}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7737760543823242},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.7550227642059326},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6776612997055054},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.6084007024765015},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5558741092681885},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.550872802734375},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5076371431350708},{"id":"https://openalex.org/C2983325608","wikidata":"https://www.wikidata.org/wiki/Q17084606","display_name":"Data association","level":3,"score":0.49270036816596985},{"id":"https://openalex.org/C100102862","wikidata":"https://www.wikidata.org/wiki/Q2625855","display_name":"Bistatic radar","level":4,"score":0.4726375937461853},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.38529086112976074},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30130505561828613},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.13436007499694824},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.11600345373153687},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09654712677001953}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mfi49285.2020.9235215","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mfi49285.2020.9235215","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1480517292","https://openalex.org/W1513101117","https://openalex.org/W1556847031","https://openalex.org/W1996298879","https://openalex.org/W2002105608","https://openalex.org/W2046508791","https://openalex.org/W2076345824","https://openalex.org/W2091979392","https://openalex.org/W2104827998","https://openalex.org/W2128002512","https://openalex.org/W2151544098","https://openalex.org/W2167866158","https://openalex.org/W2168688492","https://openalex.org/W2341035671","https://openalex.org/W2541762857","https://openalex.org/W2902358838","https://openalex.org/W3098293252","https://openalex.org/W3170709189","https://openalex.org/W4388319801","https://openalex.org/W6628523774","https://openalex.org/W6630375346","https://openalex.org/W6675713828","https://openalex.org/W6857898073","https://openalex.org/W7038517902"],"related_works":["https://openalex.org/W4319586726","https://openalex.org/W3087071515","https://openalex.org/W4283077537","https://openalex.org/W2999603699","https://openalex.org/W2902858271","https://openalex.org/W2464065341","https://openalex.org/W2947536360","https://openalex.org/W3086697448","https://openalex.org/W1987558550","https://openalex.org/W2968689489"],"abstract_inverted_index":{"This":[0],"paper":[1],"deals":[2],"with":[3,81],"bistatic":[4,30],"track":[5],"association":[6,27,49],"in":[7,89],"classical":[8],"Frequency":[9],"Modulation":[10],"(FM)":[11],"based":[12],"Multi":[13],"Static":[14],"Primary":[15],"Surveillance":[16],"Radar":[17],"(MSPSR).":[18],"We":[19],"formulate":[20,43],"deghosting":[21],"procedure":[22],"as":[23,34,36],"Bayesian":[24],"inference":[25,68],"of":[26,77,96,104],"matrix":[28,50],"between":[29],"tracks":[31],"and":[32,51,84],"targets":[33],"well":[35],"target":[37],"positions.":[38],"To":[39],"do":[40],"that,":[41],"we":[42,73,99],"prior":[44],"probability":[45],"distribution":[46],"for":[47],"the":[48,75,78,94,97,105],"develop":[52],"custom":[53],"Monte":[54],"Carlo":[55],"Markov":[56],"Chain":[57],"(MCMC)":[58],"sampler,":[59],"which":[60],"is":[61],"necessary":[62],"to":[63],"solve":[64],"such":[65,90],"a":[66,91],"hybrid":[67],"problem.":[69],"Using":[70],"simulated":[71],"data,":[72],"compare":[74],"performance":[76,88],"proposed":[79],"algorithm":[80],"two":[82],"others":[83],"show":[85],"its":[86],"superior":[87],"setup.":[92],"At":[93],"end":[95],"paper,":[98],"also":[100],"outline":[101],"further":[102],"research":[103],"algorithm.":[106]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2021,"cited_by_count":3}],"updated_date":"2026-03-25T14:56:36.534964","created_date":"2025-10-10T00:00:00"}
