{"id":"https://openalex.org/W4306147953","doi":"https://doi.org/10.1109/mfi55806.2022.9913872","title":"Probabilistic Information Matrix Fusion in a Multi-Object Environment","display_name":"Probabilistic Information Matrix Fusion in a Multi-Object Environment","publication_year":2022,"publication_date":"2022-09-20","ids":{"openalex":"https://openalex.org/W4306147953","doi":"https://doi.org/10.1109/mfi55806.2022.9913872"},"language":"en","primary_location":{"id":"doi:10.1109/mfi55806.2022.9913872","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mfi55806.2022.9913872","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 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/A5077958595","display_name":"Kaipei Yang","orcid":"https://orcid.org/0000-0001-6739-8541"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kaipei Yang","raw_affiliation_strings":["University of Connecticut,Dept. of ECE,Storrs,CT,06269"],"affiliations":[{"raw_affiliation_string":"University of Connecticut,Dept. of ECE,Storrs,CT,06269","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044127482","display_name":"Yaakov Bar\u2010Shalom","orcid":"https://orcid.org/0000-0003-1317-3368"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yaakov Bar-Shalom","raw_affiliation_strings":["University of Connecticut,Dept. of ECE,Storrs,CT,06269"],"affiliations":[{"raw_affiliation_string":"University of Connecticut,Dept. of ECE,Storrs,CT,06269","institution_ids":["https://openalex.org/I140172145"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5077958595"],"corresponding_institution_ids":["https://openalex.org/I140172145"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10588349,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"6","issue":null,"first_page":"1","last_page":"6"},"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9908999800682068,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9729999899864197,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.7479684948921204},{"id":"https://openalex.org/keywords/fusion-center","display_name":"Fusion center","score":0.7318180203437805},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6859140992164612},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.6813506484031677},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6455652117729187},{"id":"https://openalex.org/keywords/track","display_name":"Track (disk drive)","score":0.6360588669776917},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5420596599578857},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4922002851963043},{"id":"https://openalex.org/keywords/association","display_name":"Association (psychology)","score":0.4875304400920868},{"id":"https://openalex.org/keywords/state-vector","display_name":"State vector","score":0.4662524461746216},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.46237465739250183},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.45656734704971313},{"id":"https://openalex.org/keywords/data-association","display_name":"Data association","score":0.45102235674858093},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.44738391041755676},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4431706666946411},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4219767451286316},{"id":"https://openalex.org/keywords/covariance-intersection","display_name":"Covariance intersection","score":0.4186263680458069},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35223811864852905},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14063194394111633},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.0923987329006195}],"concepts":[{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.7479684948921204},{"id":"https://openalex.org/C2781234732","wikidata":"https://www.wikidata.org/wiki/Q943505","display_name":"Fusion center","level":4,"score":0.7318180203437805},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6859140992164612},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.6813506484031677},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6455652117729187},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.6360588669776917},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5420596599578857},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4922002851963043},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.4875304400920868},{"id":"https://openalex.org/C2777798563","wikidata":"https://www.wikidata.org/wiki/Q7603916","display_name":"State vector","level":2,"score":0.4662524461746216},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.46237465739250183},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45656734704971313},{"id":"https://openalex.org/C2983325608","wikidata":"https://www.wikidata.org/wiki/Q17084606","display_name":"Data association","level":3,"score":0.45102235674858093},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.44738391041755676},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4431706666946411},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4219767451286316},{"id":"https://openalex.org/C83042196","wikidata":"https://www.wikidata.org/wiki/Q5178898","display_name":"Covariance intersection","level":4,"score":0.4186263680458069},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35223811864852905},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14063194394111633},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.0923987329006195},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C149946192","wikidata":"https://www.wikidata.org/wiki/Q3235733","display_name":"Cognitive radio","level":3,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mfi55806.2022.9913872","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mfi55806.2022.9913872","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1491409777","https://openalex.org/W1531532259","https://openalex.org/W1568122762","https://openalex.org/W1828193979","https://openalex.org/W1973383077","https://openalex.org/W2011087880","https://openalex.org/W2037205244","https://openalex.org/W2067845914","https://openalex.org/W2112899894","https://openalex.org/W2137688837","https://openalex.org/W2154529834","https://openalex.org/W2154616225","https://openalex.org/W2396053301","https://openalex.org/W2891157053","https://openalex.org/W3011557943","https://openalex.org/W3154948518","https://openalex.org/W6629233157","https://openalex.org/W6638667304","https://openalex.org/W6682969975","https://openalex.org/W6711815077"],"related_works":["https://openalex.org/W3000097931","https://openalex.org/W4315783242","https://openalex.org/W2510598727","https://openalex.org/W4226323958","https://openalex.org/W2047234327","https://openalex.org/W178703191","https://openalex.org/W2372934909","https://openalex.org/W1982199320","https://openalex.org/W1899615088","https://openalex.org/W2375961464"],"abstract_inverted_index":{"In":[0,45,64],"distributed":[1,155],"sensor":[2,156],"fusion":[3,42,54,148,157],"systems,":[4],"each":[5],"of":[6,90,141],"the":[7,24,41,66,72,78,97,109,121,132],"local":[8,15,27,83],"sensors":[9,93],"has":[10],"its":[11],"own":[12],"tracker":[13],"processing":[14],"measurements":[16],"for":[17,119],"measurement-to-track":[18],"association":[19,62,67,100,163],"and":[20,34,77,92,143],"state":[21,31,80],"estimation.":[22],"Only":[23],"processed":[25],"data,":[26],"tracks":[28],"(LT)":[29],"comprising":[30],"vector":[32],"estimates":[33,81],"their":[35],"covariance":[36],"matrices":[37],"are":[38],"transmitted":[39],"to":[40,107,146],"center":[43],"(FC).":[44],"this":[46,126],"work,":[47],"a":[48,87,150],"multi-object":[49],"hybrid":[50],"probabilistic":[51],"information":[52],"matrix":[53],"(MO-HPIMF)":[55],"is":[56,70,117,129,144],"derived":[57],"taking":[58],"into":[59],"account":[60],"all":[61,139],"hypotheses.":[63],"MO-HPIMF,":[65],"carried":[68],"out":[69],"between":[71],"FC":[73],"track":[74,138],"states":[75],"(prediction)":[76],"LT":[79],"from":[82],"sensors.":[84],"When":[85],"having":[86],"large":[88],"number":[89],"objects":[91],"in":[94,105,125,131,154],"fusion,":[95],"only":[96],"m-best":[98,114],"FC-track-to-LT":[99],"hypotheses":[101],"should":[102],"be":[103],"incorporated":[104],"MO-HPIMF":[106,135],"reduce":[108],"computational":[110],"complexity.":[111],"A":[112],"Sequential":[113],"2-D":[115],"method":[116],"used":[118,152],"solving":[120],"multidimensional":[122],"assignment":[123],"problem":[124],"work.":[127],"It":[128],"shown":[130],"simulations":[133],"that":[134],"can":[136],"successfully":[137],"targets":[140],"interest":[142],"superior":[145],"track-to-track":[147],"(T2TF,":[149],"commonly":[151],"approach":[153],"system)":[158],"which":[159],"relies":[160],"on":[161],"hard":[162],"decisions.":[164]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
